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refactor: archive the old repo

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# Cache files.
/*.egg-info/
**/__pycache__/
/.mypy_cache/
# Machine-specific stuff.
/.pdm.toml
/.venv/
# Large/binary files.
/data/*
!/data/.keep

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pdm 2.3.3

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LICENSE.md
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### GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
Copyright (C) 2007 Free Software Foundation, Inc.
<https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies of this
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### Preamble
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#### 13. Remote Network Interaction; Use with the GNU General Public License.
Notwithstanding any other provision of this License, if you modify the
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version supports such interaction) an opportunity to receive the
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#### 14. Revised Versions of this License.
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#### 17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
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END OF TERMS AND CONDITIONS
### How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
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Also add information on how to contact you by electronic and paper
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# data-toolbox
# Archived repository
This repository contains the implementation of our data munging code.
Hello! All the code that used to be here is now under the [PygmalionAI organization on GitHub](https://github.com/PygmalionAI).
**Note:** Not very well documented at the moment. Still need to implement automatic downloading of data files and document how to install the project with PDM.
## How does it work?
In short, it takes raw data from several different sources and parses it. From there, we can quickly experiment with different ways of formatting or augmenting the parsed data to generate a final representation, ready to be used as training data for our models.
The general data flow goes something like this:
- We start off with raw datasets (see [./toolbox/datasets/](./toolbox/datasets/))
- These are basically classes reponsible for giving us raw data. They might, for example, download a `.zip` off the internet, unzip it, read a `.json` file from in there and then return its contents.
- Modules then make use of these datasets ([./toolbox/modules/](./toolbox/modules/))
- These are heavily inspired by the papers that introduced LaMDA and BlenderBot3 (and their relevant supporting papers)
- In general, each module is responsible for using a dataset as an input, and processing that data down into episodes, which will then be formatted into a proper dataset to be used in the fine-tuning process.
## Building a training dataset
The final data file is created with the [build_dataset.py](./toolbox/scripts/build_dataset.py) script:
```
$ ./toolbox/scripts/build_dataset.py --help
usage: build_dataset.py [-h] [-o OUTPUT_NAME] [-m MODULES] [-p PRINT] [-v]
options:
-h, --help show this help message and exit
-o OUTPUT_NAME, --output-name OUTPUT_NAME
File to write the dataset to. Should not include a file extension.
-m MODULES, --modules MODULES
List of modules to use, comma-separated.
-p PRINT, --print PRINT
If given, print this many episodes instead of writing out to a file.
-v, --verbose Enable verbose logging.
```
The default behavior is to write a file called `rev-{GIT_REVISION_HASH}-args{HASH_OF_USED_ARGS}.jsonl` to the current directory, with all the modules enabled. Behavior can be customized via the flags shown above.
The script also has an option to print some examples instead of writing to a file, for debugging/dev purposes. Example usage:
```bash
$ ./toolbox/scripts/build_dataset.py --print 1 --modules 'light_dialogue_pdm:LightDialoguePDM' # or -p 1 and -m ...
```
Example output:
```
--- new episode ---
Scenario: You are in the Watchtower.
The tower is the largest section of the castle. It contains an observatory for nighttime scouting, but is also used by the wise men to study the stars. Armed
guardsmen are always to be found keeping watch.
There's an alarm horn here.
A soldier is here. You are carrying nothing.
Court Wizard: A quiet night this evening...
Soldier: Yes it is
Court Wizard: *ponder* Have any else come up this eve? I had hoped for a quiet night to examine the stars
Soldier: *nod* Yes, a few came through, but it is a cold night for me, I am used to warmer weather
Court Wizard: *sigh* Well, you are but a common soldier. No doubt you are used to such a lot. Thankfully I have my spells to keep me warm.
Soldier: *grin* I am a soldier doing my job
Court Wizard: Yes... Well... Very well then. See that you do! No slacking off while your betters are about.
Soldier: No sir
Court Wizard: When, for example, was this horn last tested? It looks dented. How can we be sure it will work?
Soldier: A year ago, test it out or cause a need to use it
Court Wizard: *frown* Mayhap I will speak to the king about such lackness. Or perhaps I can sell him a spell that will serve just as well.
Soldier: Good idea, I agree, go do that *hug court wizard*
Court Wizard: Get off of me, you fool! Who gave you permission to touch me! *hit soldier*
Soldier: To the jail with you *hit court wizard*
```
If you're looking for the CharacterAI dumper userscript, that's [here under my personal GitHub account](https://github.com/0x000011b/characterai-dumper).

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# Project Roadmap
# Archived repository
## An intro for dummies
If you're not familiar with all the theory/technology behind the project, here's a super simplified rundown:
- There are "text generation AIs" that are freely available for researchers. These are called open-source LMs (language models).
- Modern chatbots are usually made by taking a language model and "fine-tuning" it, which basically just means feeding it data similar to what you want it to generate.
- In our case, this means fine-tuning it with conversation and roleplay data (research usually calls this "dialogue data", and they call models fine-tuned on dialogue data "dialogue models").
- LMs can have different "sizes". For example, Meta's OPT language model is offered in 125m, 350m, 1.3B, 2.7B, 6.7B, 30B and 66B sizes (where "m" = million and "B" = billion parameters).
- The bigger the model, the better its quality. However, the more hardware you need to fine-tune and use it. And when I say more, I don't mean a couple more gigabytes of system RAM, I mean going from a single 6GB GPU to hundreds of 80GB GPUs.
So, knowing the above, our main "top-level"/medium-term objective at the moment is to get as much good quality data as we can, and fine-tune the biggest model we can. From there, we can play around with the models and see what the results are like, then debate and decide how to move forward.
---
For anyone who's interested in the actual details, here's a TL;DR version of the project's current roadmap at the task level:
## Current Status
- We have all the tooling to build a dataset from various sources, fine-tune a pre-trained LM on that dataset, and then run inference on checkpoints saved during the fine-tune process.
- All of that tooling can be found within this repository.
- We have taken a small model, Meta's OPT-350m, and fine-tuned it on a small dataset we've built with the tooling described above. We've released it as a tiny prototype.
- The model checkpoint is hosted on HuggingFace under [Pygmalion-AI/pygmalion-350m](https://huggingface.co/Pygmalion-AI/pygmalion-350m).
- **Note:** Inference should not be done on the regular HuggingFace web UI since we need to do some prompt trickery and response parsing. To play around with the model, [try out this notebook](https://colab.research.google.com/drive/1K55_MCagEDD9EmWhjCi3Bm66vJM88m6P?usp=sharing).
- We have written a [userscript which can anonymize and dump your CharacterAI chats](./extras/characterai-dumper/), and made [a website where you can upload them](https://dump.nopanda.io/) to be used as training data for future models. If you're interested in contributing, please read through [this Rentry](https://rentry.org/f8peb) for more information.
- We released a prototype 1.3B model fine-tuned on a new dataset, which includes the anonymized CharaterAI data.
- It's hosted on HuggingFace under [Pygmalion-AI/pygmalion-1.3b](https://huggingface.co/Pygmalion-AI/pygmalion-1.3b).
- We've already received feedback from several users (thank you everyone who took the time to test out the model and write to us!) and identified several shortcomings in it.
## Next Steps
- We're on the lookout for more high-quality data sources, and we still welcome [new CharacterAI dumps](https://dump.nopanda.io/).
- We plan on training a new 1.3B model, but only after brainstorming and making changes to our data processing pipeline in an attempt to improve on the problems we've seen on the current 1.3B.
- Feel free to join the Matrix server if you want to join in on the discussions.
- Once we have a decently performing 1.3B model, we plan on setting up some sort of way for people to rate/rank bot responses and send that data to us, which will then be used to train a reward model to be used for a PPO fine-tune, similar to what OpenAI did for ChatGPT. We might join forces with other communities for this step, as well.
Hello! We've moved to the [PygmalionAI organization on GitHub](https://github.com/PygmalionAI). Please go check that out for information about the project instead.

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# Important Notice
Hello! We're migrating the CAI userscript over to a [repository under my personal GitHub account](https://github.com/0x000011b/characterai-dumper). If anyone sent you here, please let them know so they can update their links!
# CharacterAI Dumper Userscript
This userscript allows you to download your saved messages with any bot you've ever talked to, given you can reach their chat history page. If you're a bot creator, it also allows you to separately download your bot's definitions.

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43
pyproject.toml
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[tool.pdm]
[project]
name = "toolbox"
version = "0.1.0"
description = "Code for ingesting data from several sources, formatting it and creating a training dataset."
authors = [
{name = "0x000011b", email = "0x000011b@proton.me"},
]
requires-python = ">=3.10"
license = {text = "AGPL-3.0-only"}
dependencies = [
"ijson>=3.1.4",
"mashumaro>=3.2",
"regex>=2022.10.31",
"scikit-learn>=1.2.0",
"pandas>=1.5.2",
"pyarrow>=10.0.1",
]
[project.optional-dependencies]
dev = [
"yapf>=0.32.0",
"toml>=0.10.2",
"isort>=5.10.1",
"pylint>=2.15.8",
"mypy>=0.991",
]
debugging = [
"pdbpp>=0.10.3",
]
[tool.setuptools]
py-modules = ["toolbox"]
[tool.pdm.scripts]
lint = {shell = "pylint --jobs 0 ./toolbox/**/*.py"}
importcheck = "isort --check --diff toolbox"
stylecheck = "yapf --parallel --diff --recursive toolbox"
typecheck = "mypy --strict toolbox"
[tool.yapf]
based_on_style = "google"

0
toolbox/__init__.py
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28
toolbox/core/consts.py
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class PromptConstants:
'''String constants related to prompt engineering.'''
# Prefix for user messages.
USER_PREFIX = "You"
# Token to be replaced with the user's display name within bot messages.
USER_TOKEN = "<USER>"
# Token to be replaced by the bot's name.
BOT_TOKEN = "<BOT>"
# Should be kept in sync with the relevant model that will be trained. This
# is taken from EleutherAI's Pythia (so, GPT-NeoX).
EOS_TOKEN = "<|endoftext|>"
# Token to separate prompt trickery from actual dialogue.
CHAT_START_TOKEN = "<START>"
# Global target word count. The word count is chosen in such a way that we
# can fit all the required prompt trickery into the model's input, but still
# leave enough space for the user's input message and the inference result.
TARGET_WORD_COUNT_PER_EPISODE = 1024
@staticmethod
def pdm_prefix_for(name: str) -> str:
'''Builds the Persona Dialogue Module prefix for a given `name`.'''
return f"{name}'s Persona"

15
toolbox/datasets/__init__.py
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@ -1,15 +0,0 @@
import typing as t
T = t.TypeVar("T")
class BaseDataset(t.Generic[T]):
'''Base dataset class.'''
def __iter__(self) -> t.Generator[T, None, None]:
'''Implements the basic iterator interface.'''
return self.generator()
def generator(self) -> t.Generator[T, None, None]:
'''Should yield individual items from the dataset.'''
raise NotImplementedError

161
toolbox/datasets/characterai.py
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@ -1,161 +0,0 @@
import json
import logging
import os
import typing as t
from dataclasses import dataclass
from toolbox.datasets import BaseDataset
from toolbox.utils.dataset import get_data_path
logger = logging.getLogger(__name__)
@dataclass(frozen=True)
class CaiBotInfo:
name: str
title: str
description: str | None
greeting: str
# Optional because it might be private.
definitions: str | None
# Useful for when several bots have the same name - we can tell them apart
# by their external_id.
external_id: str
# There's also categories, but I'm ignoring them for now since I don't think
# they'll be of much use.
@dataclass(frozen=True)
class CaiMessage:
is_human: bool
text: str
@dataclass(frozen=True)
class CaiChat:
# First message is always the bot's greeting.
messages: list[CaiMessage]
bot: CaiBotInfo
class CharacterAiDataset(BaseDataset[CaiChat]):
'''Dataset for CharacterAI dumps.'''
def generator(self) -> t.Generator[CaiChat, None, None]:
bot_id_to_info_dict = {}
# Do a first run through all the files to load all the definitions and
# descriptions.
for data in _available_json_data():
if not _is_definition_data(data):
continue
bot_info = _bot_info_from_dict(data["character"])
bot_id_to_info_dict[bot_info.external_id] = bot_info
# Now do a second pass, to actually handle chat histories/messages.
for data in _available_json_data():
if _is_definition_data(data):
continue
# Prefer grabbing bot info from a Character Editor dump, if it
# exists. Fall back to public data otherwise.
bot_id = data["info"]["character"]["external_id"]
bot_info = bot_id_to_info_dict.get(
bot_id, _bot_info_from_dict(data["info"]["character"]))
for history_dict in data["histories"]["histories"]:
messages = _messages_from_dict(history_dict["msgs"])
yield CaiChat(bot=bot_info, messages=messages)
#
# Private helpers.
#
def _enumerate_json_files(root_path: str) -> list[str]:
'''Returns a list of files available in the given `root_path`.'''
items = os.listdir(root_path)
files: list[str] = []
for item in items:
item_path = os.path.join(root_path, item)
if not os.path.isfile(item_path) or not item_path.endswith(".json"):
# We only care about JSON files.
continue
absolute_file_path = os.path.abspath(os.path.join(root_path, item))
files.append(absolute_file_path)
return files
def _available_json_data() -> t.Generator[dict[str, t.Any], None, None]:
'''
Yields all available JSON data, parsed from the files in the CharacterAI
data folder.
'''
dataset_path = get_data_path(dataset_name="characterai")
for folder in ["public", "private"]:
folder_path = os.path.join(dataset_path, folder)
for json_file_path in _enumerate_json_files(folder_path):
with open(json_file_path, "r", encoding="utf-8") as json_file:
try:
yield json.load(json_file)
except json.decoder.JSONDecodeError as ex:
logger.error("Failed to parse %s: %s", json_file_path, ex)
def _bot_info_from_dict(info_dict: dict[str, t.Any]) -> CaiBotInfo:
'''Builds a CaiBotInfo object from the `character` field in the JSON.'''
return CaiBotInfo(
name=info_dict["name"],
title=info_dict["title"],
# This comes in as an empty string instead of `null` in the JSON when
# it's not defined for some reason, so we cast to None here for clarity.
description=info_dict["description"] or None,
greeting=info_dict["greeting"],
definitions=info_dict.get("definition"),
external_id=info_dict["external_id"],
)
def _messages_from_dict(msgs_dict: list[dict[str, t.Any]]) -> list[CaiMessage]:
'''Builds an array of messages from an entry from the `histories` JSON.'''
messages: list[CaiMessage] = []
for raw_message in msgs_dict:
message = CaiMessage(
text=raw_message["text"],
is_human=raw_message["src"]["is_human"],
)
messages.append(message)
return messages
def _is_definition_data(dict_from_json: dict[str, t.Any]) -> bool:
'''
Figures out whether the given dict (parsed from a JSON file) is a regular
dump, or a dump from the Character Editor (possibly containing definitions).
If it doesn't seem like either, raises a `ValueError` so we can discard bad
data.
'''
keys = list(dict_from_json.keys())
# Some people messed with their files so the order of the keys isn't always
# the same, so we sort for consistency.
keys.sort()
if keys == ["character"]:
return True
elif keys == ["character", "user__username"]:
return True
elif keys == ["histories", "info"]:
return False
else:
print(dict_from_json)
raise ValueError(f"Unexpected keys found in CAI dump JSON file: {keys}")

300
toolbox/datasets/kajiwoto.py
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@ -1,300 +0,0 @@
import json
import logging
import os
import re
import typing as t
from dataclasses import dataclass
from toolbox.core.consts import PromptConstants
from toolbox.datasets import BaseDataset
from toolbox.utils.dataset import get_data_path
# The regex used to find message variants (e.g.: `%{Hi|Hello} there!`)
KAJIWOTO_VARIANT_REGEX = re.compile(r'%{(.+?)}')
# These bots shouldn't be a part of the final dataset, for whatever reason.
BLACKLISTED_BOT_IDS = set(["WvqA"])
logger = logging.getLogger(__name__)
@dataclass(frozen=True)
class KajiwotoMessageResponsePair:
message_id: str
bot_id: str
user_message: str
bot_response: str
condition: str
@dataclass(frozen=True)
class BotMetadata:
bot_id: str
name: str
description: str
personalities: t.List[t.List[str]]
has_nsfw: bool
tags: t.List[str]
class KajiwotoDataset(BaseDataset[t.List[KajiwotoMessageResponsePair]]):
'''
The Kajiwoto dataset.
Takes care of properly handling chat history/message context.
'''
def __init__(self) -> None:
self.filepaths = _enumerate_kajiwoto_json_files()
self.cached_metadata: dict[str, BotMetadata] = {}
def get_metadata_for_bot(self, bot_id: str) -> BotMetadata:
'''Returns known medatada for the given bot ID.'''
if bot_id in self.cached_metadata:
return self.cached_metadata[bot_id]
dataset_path = get_data_path(dataset_name="kajiwoto")
metadata_filepath = os.path.join(dataset_path,
f"{bot_id}_metadata.json")
with open(metadata_filepath, "r", encoding="utf-8") as metadata_file:
metadata_dict = json.loads(
metadata_file.read())["data"]["aiTrainerGroup"]
metadata = _metadata_dict_to_dataclass(metadata_dict)
return metadata
def generator(
self
) -> t.Generator[t.List[KajiwotoMessageResponsePair], None, None]:
for filepath in self.filepaths:
with open(filepath, "r", encoding="utf-8") as file:
messages = json.loads(file.read())["data"]["aiTrainedList"]
# So, there's a tricky thing to handle in these datasets which
# is the fact that follow-up messages are saved as completely
# separate entries in the messages array. For example, if we
# have a chat log like:
#
# Human: 1
# Bot: 2
# Human: 3
# Bot: 4
#
# We will have, in the messages array, something like:
# [
# {"userMessage": "3", message: "4", "history": ["1"]},
# {"userMessage": "1", message: "2"},
# ]
#
# As far as I could tell, whenever a message has a "history"
# field, it usually doesn't make sense by itself. Or even by
# appending history. One needs to look up the original message
# and reply pair using the history field, then build up the
# sequence again manually.
#
# As such, for each file, we need to load the entire thing into
# memory to run over it and build an index to do just that
# (lookups via the history field), so here we go:
history_contents_to_original_msg_idx: dict[str, int] = {}
used_message_indexes: t.Set[int] = set()
for idx, msg in enumerate(messages):
if msg["history"]:
# Message already references an earlier message-reply
# pair. As far as I could tell, that means _this_
# specific message can't be referenced, so no point in
# saving an index for it here.
continue
history_contents_to_original_msg_idx[
msg["userMessage"]] = idx
# Now that we have the history index, let's go over _only_ the
# messages that need to be concatenated with their history.
for idx, msg in enumerate(messages):
if not msg.get("history", None):
continue
history_contents = msg["history"][0]
# Sometimes, a message seems to reference a previous one
# that does not exist. Don't know what's up with that, so
# let's just ignore.
if not history_contents in history_contents_to_original_msg_idx:
continue
# Fetch the original "history" message to use as context.
original_msg_idx = history_contents_to_original_msg_idx[
history_contents]
original_msg = messages[original_msg_idx]
# Yield the conversation episode.
yield [
_dict_to_dataclass(original_msg),
_dict_to_dataclass(msg),
]
# Save the indexes of both of these so we don't re-use them
# without the proper context.
used_message_indexes.add(idx)
used_message_indexes.add(original_msg_idx)
# Now let's go over regular, history-free messages.
for idx, msg in enumerate(messages):
if idx in used_message_indexes:
continue
yield [_dict_to_dataclass(msg)]
#
# Public helpers.
#
seen_special_tokens: set[str] = set()
seen_scenes: set[str] = set()
def replace_special_tokens_in(string: str) -> str:
'''
Replaces known special tokens (e.g.: `%{name}`) with their expected
equivalents.
'''
string = string.replace("%{name}", PromptConstants.USER_TOKEN)
string = string.replace("%{kajiname}", PromptConstants.BOT_TOKEN)
if (match := re.search(KAJIWOTO_VARIANT_REGEX, string)) is not None:
special_token = match.groups()[0]
if '|' not in special_token and special_token not in seen_special_tokens:
logger.warning("Unhandled Kajiwoto token: %s", special_token)
seen_special_tokens.add(special_token)
if (scene_match := re.search(r"#scene=(.+?)\b", string)) is not None:
seen_scene = scene_match.groups()[0]
if seen_scene not in seen_scenes:
logger.debug("Unhandled Kajiwoto scene: %s", seen_scene)
seen_scenes.add(seen_scene)
# Drop the scene marker. Maybe we can use it for something useful, but
# I can't think of anything at the moment.
string = string.replace(f"#scene={seen_scene}", "").strip()
# TODO: There's a few of these which I haven't handled yet. E.g.:
# %{pronoun} (before and after a dot, so careful with caps).
return string
def generate_variants_for(
string: str,
max_generations: int = 16,
start_counter_at: int = 0) -> t.Generator[str, None, None]:
'''
Given a string like "%{Hello|Hi} there{.|!}, this should yield:
- Hello there.
- Hello there!
- Hi there.
- Hi there!
'''
# Some bot creators went wild with the variants, which causes ridiculous
# generations if we try to exhaust all possibilities so we cap that here.
# `start_counter_at` is used for keeping track across recursive calls.
counter = start_counter_at
if (match := re.search(KAJIWOTO_VARIANT_REGEX, string)) is not None:
# Once we have a "%{X|Y|Z}" matched inside the original string, we:
# - Fetch .groups()[0] (which will give us `X|Y|Z`)
# - Split by `|` (so we have ["X", "Y", "Z"])
# - Filter out empty strings
alternatives = filter(lambda x: x.strip(), match.groups()[0].split("|"))
# Then, we break the string apart into what comes before and after the
# alternatives, that way we can re-build with "prefix + choice + sufix".
prefix = string[:match.start()]
sufix = string[match.end():]
for alternative in alternatives:
variant = f'{prefix}{alternative}{sufix}'
# However, some strings have multiple variant blocks. In that case,
# we operate on them recursively until we have just regular strings
# after generating all possible variants.
still_have_match = re.search(KAJIWOTO_VARIANT_REGEX,
variant) is not None
if still_have_match:
for inner_variant in generate_variants_for(
variant, start_counter_at=counter):
yield inner_variant
# Keep track and break after `max_generations`.
counter += 1
if max_generations is not None and counter >= max_generations:
break
else:
yield variant
# Keep track and break after `max_generations`.
counter += 1
if max_generations is not None and counter >= max_generations:
break
else:
yield string
#
# Private helpers.
#
def _enumerate_kajiwoto_json_files() -> list[str]:
'''
Returns a list of paths to all available `.json` files for the `kajiwoto`
dataset.
'''
dataset_path = get_data_path(dataset_name="kajiwoto")
items = os.listdir(dataset_path)
files: list[str] = []
for item in items:
if not item.endswith(".json"):
# Don't care about other file types.
continue
if item.endswith("_metadata.json"):
# Don't want to list metadata files here.
continue
if item.replace(".json", "") in BLACKLISTED_BOT_IDS:
# Don't want blacklisted bots being included.
continue
item_path = os.path.join(dataset_path, item)
if not os.path.isfile(item_path):
# Don't care about folders.
continue
absolute_item_path = os.path.abspath(os.path.join(dataset_path, item))
files.append(absolute_item_path)
return files
def _dict_to_dataclass(obj: dict[str, str]) -> KajiwotoMessageResponsePair:
return KajiwotoMessageResponsePair(
message_id=obj["id"],
bot_id=obj["aiTrainerGroupId"],
condition=obj["condition"],
user_message=obj["userMessage"],
bot_response=obj["message"],
)
def _metadata_dict_to_dataclass(obj: dict[str, t.Any]) -> BotMetadata:
return BotMetadata(
bot_id=obj["id"],
name=obj["name"],
description=obj["description"],
personalities=obj["personalities"],
has_nsfw=obj["nsfw"],
tags=obj["tags"],
)

61
toolbox/datasets/light_dialogue.py
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@ -1,61 +0,0 @@
import os
import pickle
import typing as t
from dataclasses import dataclass
import mashumaro
from toolbox.datasets import BaseDataset
from toolbox.utils.dataset import get_data_path
@dataclass(frozen=True)
class LightDialogueAgent(mashumaro.DataClassDictMixin):
name: str
persona: str
@dataclass(frozen=True)
class LightDialogueSetting(mashumaro.DataClassDictMixin):
name: str
category: str
description: str
background: str
@dataclass(frozen=True)
class LightDialogueEpisode(mashumaro.DataClassDictMixin):
agents: t.List[LightDialogueAgent]
setting: LightDialogueSetting
character: t.List[str]
context: t.List[str]
room_objects: t.List[t.List[str]]
room_agents: t.List[t.List[str]]
all_descriptions: t.Dict[str, str]
available_actions: t.List[t.List[str]]
carrying: t.List[t.List[str]]
wielding: t.List[t.List[str]]
speech: t.List[str]
emote: t.List[str]
action: t.List[str]
class LightDialogueDataset(BaseDataset[LightDialogueEpisode]):
'''
LIGHT: Learning in Interactive Games with Humans and Text
The LIGHT project is a large-scale fantasy text adventure game research
platform for training agents that can both talk and act, interacting either
with other models or with humans.
https://parl.ai/projects/light/
'''
def generator(self) -> t.Generator[LightDialogueEpisode, None, None]:
root_data_path = get_data_path("light_dialogue")
light_data_path = os.path.join(root_data_path, "light_data.pkl")
with open(light_data_path, "rb") as light_data_file:
light_data = pickle.load(light_data_file)
for episode in light_data:
yield LightDialogueEpisode.from_dict(episode)

42
toolbox/datasets/soda.py
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@ -1,42 +0,0 @@
import os
import pickle
import typing as t
from dataclasses import dataclass
import mashumaro
import pandas as pd
from toolbox.datasets import BaseDataset
from toolbox.utils.dataset import get_data_path
@dataclass(frozen=True)
class SodaEpisode(mashumaro.DataClassDictMixin):
narrative: str
dialogue: t.List[str]
speakers: t.List[str]
relation: str
literal: str
class SodaDataset(BaseDataset[SodaEpisode]):
'''
SODA: Million-scale Dialogue Distillation with Social Commonsense
Contextualization
https://huggingface.co/datasets/allenai/soda
'''
def generator(self) -> t.Generator[SodaEpisode, None, None]:
root_data_path = get_data_path("soda")
file_path = os.path.join(root_data_path, "test.parquet")
df = pd.read_parquet(file_path)
# Iterate through the test part of the SODA dataset
for i in df.index:
yield SodaEpisode(
narrative=df['narrative'][i],
dialogue=df['dialogue'][i],
speakers=df['speakers'][i],
relation=df['relation'][i],
literal=df['literal'][i]
)

16
toolbox/modules/__init__.py
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@ -1,16 +0,0 @@
import typing as t
class BaseModule:
'''Base module class.'''
def __iter__(self) -> t.Generator[list[str], None, None]:
'''Implements the basic iterator interface.'''
return self.generator()
def generator(self) -> t.Generator[list[str], None, None]:
'''
Should yield dialogue turns that will be used in the model's training /
validation / test splits.
'''
raise NotImplementedError

203
toolbox/modules/characterai_pdm.py
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import logging
import re
import typing as t
from toolbox.core.consts import PromptConstants
from toolbox.datasets.characterai import CharacterAiDataset
from toolbox.modules import BaseModule
logger = logging.getLogger(__name__)
# Discard episodes shorter than 3 turns. These are likely not very useful for
# the model to learn to converse properly, since they only really contain one
# dialogue response (the first turn is the hardcoded greeting, and the second is
# the user's input).
MIN_EPISODE_LEN = 3
# Discard episodes where the average similarity between the bot's messages is
# higher than this value.
EPISODE_SIMILARITY_THRESHOLD = 0.55
#
# So here's a quick rundown of what needs to happen. We have a limited context
# window (of 2048 tokens, ATM) and for the Persona Dialogue Module (PDM), we
# need to fit all of the following things in there:
#
# - The bot's description/definitions/persona/whatever you want to call it
# - Last X messages of chat history/context (the more the merrier, usually)
# - The user's input message, e.g. `You: [user text here]`
# - The bot's response, e.g. `[Bot name]: [space for the bot's response]`
#
# As such, most of the code here is about taking globs of text and
# chunking/splitting them up to make the format described above fit into blocks
# of 2048-ish tokens (not exactly 2048 because the tokenizer depends on the
# model used, and I don't want to create a dependency on a specific model at the
# data processing stage at this point).
#
class CharacterAiPDM(BaseModule):
'''A Persona Dialogue Module powered by CharacterAI data.'''
def generator(self) -> t.Generator[list[str], None, None]:
for chat in CharacterAiDataset():
if len(chat.messages) < MIN_EPISODE_LEN:
logger.debug(
"Found episode shorter than minimum length (%s < %s), discarding.",
len(chat.messages), MIN_EPISODE_LEN)
continue
base_turns = []
if chat.bot.description is not None:
pdm_prefix = PromptConstants.pdm_prefix_for(chat.bot.name)
pdm_string = f"{pdm_prefix}: {chat.bot.description}"
base_turns.append(pdm_string)
if chat.bot.definitions is not None:
parsed_definitions, parsed_examples = _parse_definitions_for(
chat.bot.name, chat.bot.definitions)
base_turns.append(parsed_definitions)
# Add turn to separate persona info from messages, if
# necessary.
if len(base_turns) > 0:
base_turns.append(PromptConstants.CHAT_START_TOKEN)
# Now, start adding messages and break episodes apart if they get
# too big.
turns = base_turns.copy()
bot_messages: list[str] = []
for raw_message in chat.messages:
message_text = _process_message(raw_message.text)
if raw_message.is_human:
message = f"{PromptConstants.USER_PREFIX}: {message_text}"
else:
message = f"{chat.bot.name}: {message_text}"
bot_messages.append(message_text)
turns.append(message)
# Splitting logic.
cur_episode_len = sum([len(x.split()) for x in turns])
if cur_episode_len > PromptConstants.TARGET_WORD_COUNT_PER_EPISODE:
logger.debug(
"Episode length went over TARGET_WORD_COUNT_PER_EPISODE (%s > %s), breaking apart.",
cur_episode_len,
PromptConstants.TARGET_WORD_COUNT_PER_EPISODE)
# Calculate similarity between sequential bot message pairs
# within this episode, and drop it if it goes above the
# defined threshold.
similarity_score_matrix = _calculate_similarity_scores(
bot_messages)
average_similarity_score_for_episode = 0.0
for score in similarity_score_matrix[0]:
if score == 1:
continue
average_similarity_score_for_episode += score
average_similarity_score_for_episode /= 2
# Adding the last message made the episode go over the
# target word count, so we return the episode without it...
removed_turn = turns.pop()
if average_similarity_score_for_episode <= EPISODE_SIMILARITY_THRESHOLD:
# yield "\n".join(turns)
yield turns
else:
logger.debug(
"Ignoring episode due to high similarity between messages (%s > %s)",
average_similarity_score_for_episode,
EPISODE_SIMILARITY_THRESHOLD)
# ...and start the next episode with the message we had to
# trim out from this one.
turns = base_turns.copy()
turns.append(removed_turn)
bot_messages = []
#
# Private helpers.
#
EXAMPLE_CHAT_REGEX = re.compile(
r"({{char}}|{{random_user_\d}}): (.+?)(?:END_OF_DIALOG)", re.DOTALL)
RELAXED_EXAMPLE_CHAT_REGEX = re.compile(r"{{char}}: .+", re.DOTALL)
EXCESSIVE_ELLIPSIS_REGEX = re.compile(r"\.{4,}")
def _process_message(original_string: str) -> str:
'''
Processes a single message to clean it up and filter/replace the appropriate
special tokens.
'''
string = EXCESSIVE_ELLIPSIS_REGEX.sub("...", original_string)
string = string.replace("[NAME_IN_MESSAGE_REDACTED]",
PromptConstants.USER_TOKEN)
return string.strip()
def _calculate_similarity_scores(bot_turns: list[str]) -> t.Any:
'''
Calculates similarity scores between bot turns.
This is a roundabout way to try and _possibly_ detect the post-1.1 CAI
looping behavior so we can handle it during the data preprocessing.
'''
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
vectorizer = CountVectorizer()
x = vectorizer.fit_transform(bot_turns)
arr = x.toarray()
sims = cosine_similarity(arr)
return sims
def _parse_definitions_for(bot_name: str,
raw_definitions: str) -> t.Tuple[str, list[str]]:
'''
Parses bot definitions.
This function attempts to find example messages within the input string,
parses them accordingly and returns them separately from the rest of the
text in the original `definitions` string.
'''
definitions, examples = _parse_definitions_strict(raw_definitions)
if len(examples) == 0:
definitions, examples = _parse_definitions_relaxed(raw_definitions)
parsed_definitions = definitions.replace("{{char}}", bot_name)
parsed_examples = [x.replace("{{char}}", bot_name) for x in examples]
return parsed_definitions, parsed_examples
def _parse_definitions_strict(definitions: str) -> t.Tuple[str, list[str]]:
'''
Strict parsing of a bot's definitions string, assumes END_OF_DIALOG was used
correctly by the bot's creator.
'''
matched_example_chats = EXAMPLE_CHAT_REGEX.finditer(definitions)
examples = [
x.group().replace("END_OF_DIALOG", "").strip()
for x in matched_example_chats
]
definitions_without_examples = re.sub(EXAMPLE_CHAT_REGEX, "", definitions)
return definitions_without_examples, examples
def _parse_definitions_relaxed(definitions: str) -> t.Tuple[str, list[str]]:
'''
Same as the `_parse_definitions_strict`, but this one is much more relaxed
and should be used for when the bot creator didn't properly use
END_OF_DIALOG to delineate example chats.
'''
matched_example_chats = RELAXED_EXAMPLE_CHAT_REGEX.finditer(definitions)
examples = [x.group().strip() for x in matched_example_chats]
definitions_without_examples = re.sub(RELAXED_EXAMPLE_CHAT_REGEX, "",
definitions)
return definitions_without_examples, examples

220
toolbox/modules/discord_vdm.py
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'''
This module generates dialogue data from Discord dumps. Specifically, it:
- Looks for a DHT (https://github.com/chylex/Discord-History-Tracker) database
in `/data/discord/archive.dht` to parse
- Builds a list of senders who meet certain criteria (enough messages sent,
messages long enough), then
- Attempts to find uninterruped conversations between them and another person in
public channels.
Since a DHT database necessarily contains personal information, this module must
be manually enabled and populated with your own data.
'''
import logging
import os
import re
import sqlite3
import typing as t
from toolbox.modules import BaseModule
from toolbox.utils.dataset import get_data_path
# Matches user mentions, channel links, emotes and maybe other stuff.
SPECIAL_TOKENS_REGEX = re.compile(r"<[@:#].+?>")
MINIMUM_EPISODE_LENGTH = 5
logger = logging.getLogger(__name__)
class DiscordVDM(BaseModule):
'''A Vanilla Dialogue Module powered by Discord dumps.'''
def generator(self) -> t.Generator[list[str], None, None]:
root_data_path = get_data_path("discord")
db_path = os.path.join(root_data_path, "archive.dht")
db = sqlite3.connect(db_path)
db.row_factory = sqlite3.Row
cursor = db.cursor()
sender_ids = _get_filtered_sender_ids(cursor)
for sender_id in sender_ids:
last_message_id = None
while (episode_contents := _build_episode_turns(
db, sender_id,
start_after_message_id=last_message_id)) is not None:
turns, last_message_id = episode_contents
# Discard short episodes.
if len(turns) < MINIMUM_EPISODE_LENGTH:
logger.debug(
"Found short %s-turn episode (< %s), discarding.",
len(turns), MINIMUM_EPISODE_LENGTH)
continue
# Discard conversations with overly short messages.
lengths = [len(x) for x in turns]
avg = sum(lengths) / len(lengths)
if avg < 64:
logger.debug(
"Found conversation where average message length was %s, discarding.",
avg)
continue
yield turns
#
# Private helpers.
#
def _clean_string(string: str) -> str:
'''Removes user mentions, channel links and so on.'''
return re.sub(SPECIAL_TOKENS_REGEX, "", string).strip()
def _looks_like_ooc(raw_string: str) -> bool:
'''Tries to figure out whether a message looks like it's out of character.'''
string = raw_string.strip()
if string[0] == "(" and string[-1] == ")":
return True
if "OOC:" in string:
return True
return False
def _get_filtered_sender_ids(cursor: sqlite3.Cursor) -> list[int]:
'''Gets a list of sender_ids that meet the filtering criteria.'''
res = cursor.execute('''
SELECT
sender_id
FROM (
SELECT
"sender_id",
AVG(LENGTH("text")) AS average_message_length,
COUNT("sender_id") AS messages_sent
FROM
"messages"
GROUP BY
"sender_id"
ORDER BY
"average_message_length" DESC
)
WHERE
"messages_sent" > 8 AND "average_message_length" >= 32;
''').fetchall()
return [x[0] for x in res]
def _build_episode_turns(
db: sqlite3.Connection,
sender_id: int,
start_after_message_id: int | None = None
) -> tuple[list[str], int] | None:
logger.debug("Building episode for sender_id %s, starting after message %s",
sender_id, start_after_message_id)
# Fetch the first message for the episode.
if start_after_message_id:
query = """
SELECT
message_id, channel_id
FROM
messages
WHERE
sender_id = :sender_id AND message_id > :message_id;
"""
else:
query = """
SELECT
message_id, channel_id
FROM
messages
WHERE
sender_id = :sender_id LIMIT 1;
"""
cursor = db.cursor()
res = cursor.execute(query, {
"sender_id": sender_id,
"message_id": start_after_message_id,
}).fetchone()
if res is None:
logger.debug("No more suitable first messages found.")
return None
message_id, channel_id = res["message_id"], res["channel_id"]
logger.debug("Found suitable first message %s by %s.", message_id,
sender_id)
# From there, fetch that specific channel's log from that point on.
query = """
SELECT
*
FROM
messages
WHERE
channel_id = :channel_id
AND
message_id >= :message_id
;
"""
res = cursor.execute(query, {
"channel_id": channel_id,
"message_id": message_id,
})
person_a_id = sender_id
person_b_id = None
last_message_id = -1
turns: list[str] = []
while (row := res.fetchone()) is not None:
last_message_id = row["message_id"]
# Save who `sender_id` is talking to.
if person_b_id is None and row["sender_id"] != person_a_id:
person_b_id = row["sender_id"]
# Somebody else came into the conversation. Stop episode here.
if person_b_id and row["sender_id"] not in (person_a_id, person_b_id):
logger.debug(
"%s barged into a conversation between %s and %s, assuming end of episode.",
row["sender_id"],
person_a_id,
person_b_id,
)
break
cleaned_text = _clean_string(row["text"])
if not cleaned_text:
# Message was empty after cleaning it up, skip.
continue
if _looks_like_ooc(cleaned_text):
logger.debug("Dropping what _seems_ to be OOC talk: `%s`",
cleaned_text)
continue
# Get username.
# TODO(11b): Anonymize.
username_query = "SELECT name FROM users WHERE id = :user_id"
username = db.cursor().execute(username_query, {
"user_id": row["sender_id"]
}).fetchone()["name"]
# Build up the string and add it to the episode.
turn_string = f"{username}: {cleaned_text}"
turns.append(turn_string)
if len(turns) == 0:
logger.debug(
"Empty episode, assuming no more conversations from this sender.")
return None
return turns, last_message_id

48
toolbox/modules/kajiwoto_pdm.py
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import typing as t
from toolbox.core.consts import PromptConstants
from toolbox.datasets.kajiwoto import (KajiwotoDataset, generate_variants_for,
replace_special_tokens_in)
from toolbox.modules import BaseModule
from toolbox.utils.strings import uppercase
class KajiwotoPDM(BaseModule):
'''A Persona Dialogue Module powered by the Kajiwoto dataset.'''
def generator(self) -> t.Generator[str, None, None]:
dataset = KajiwotoDataset()
for episode in dataset:
turns: list[str] = []
metadata = dataset.get_metadata_for_bot(episode[0].bot_id)
# `metadata.personalities` is in a format like: `[["friendly", "20.32"]]`
# but we want that "phrased" closer to natural language, so we build
# `persona_string` to take care of that.
personality_descriptors = [x[0] for x in metadata.personalities]
persona_string = ". ".join(
[uppercase(x) for x in personality_descriptors]) + "."
description_string = metadata.description.replace("\n",
" ").replace(
" ", " ")
turns.append(
f"{PromptConstants.pdm_prefix_for(PromptConstants.BOT_TOKEN)}: {description_string}\n{persona_string}"
)
# Empty turn to have a line break separating description/persona
# and the actual messages.
turns.append("")
for turn in episode:
turns.append(
f"{PromptConstants.USER_PREFIX}: {turn.user_message}")
turns.append(
f"{PromptConstants.BOT_TOKEN}: {turn.bot_response}")
string = "\n".join(turns)
processed_string = replace_special_tokens_in(string)
for generated_string in generate_variants_for(processed_string):
yield generated_string

26
toolbox/modules/kajiwoto_vdm.py
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import typing as t
from toolbox.core.consts import PromptConstants
from toolbox.datasets.kajiwoto import (KajiwotoDataset, generate_variants_for,
replace_special_tokens_in)
from toolbox.modules import BaseModule
class KajiwotoVDM(BaseModule):
'''A Vanilla Dialogue Module powered by the Kajiwoto dataset.'''
def generator(self) -> t.Generator[list[str], None, None]:
dataset = KajiwotoDataset()
for episode in dataset:
turns: t.List[str] = []
for turn in episode:
turns.append(
f"{PromptConstants.USER_PREFIX}: {turn.user_message}")
turns.append(
f"{PromptConstants.BOT_TOKEN}: {turn.bot_response}")
string = "\n".join(turns)
processed_string = replace_special_tokens_in(string)
for generated_string in generate_variants_for(processed_string):
yield generated_string.split("\n")

51
toolbox/modules/light_dialogue_pdm.py
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import typing as t
from toolbox.core.consts import PromptConstants
from toolbox.datasets.light_dialogue import LightDialogueDataset
from toolbox.modules import BaseModule
from toolbox.utils.strings import normalize_string, title_case
class LightDialoguePDM(BaseModule):
'''Persona Dialogue Module based on the LIGHT dataset.'''
def generator(self) -> t.Generator[list[str], None, None]:
for episode in LightDialogueDataset():
# TODO(11b): Scenario doesn't belong in a persona dialog module.
context_message = f"Scenario: {episode.context[0]}\n"
persona_message = ""
for agent in episode.agents:
persona_message += f"{PromptConstants.pdm_prefix_for(title_case(agent.name))}: {agent.persona}\n"
episode_messages: t.List[str] = [context_message, persona_message]
turn_count = len(episode.speech)
for idx in range(turn_count):
character = title_case(episode.character[idx])
speech = normalize_string(episode.speech[idx])
# Start off with just the actual speech dialogue.
message = speech
# If there was an action performed in that turn, add it to the
# string.
#
# NOTE(11b): Disabled for now. Adding the action like this
# generates grammatically incorrect sentences.
# action = episode.action[idx]
# if action is not None:
# message += f" *{action}*"
# If there was an emote in that turn, add it to the string.
emote = episode.emote[idx]
if emote is not None:
message = f"*{emote}* {message}"
# Finally, prepend the turn character's name.
message = f"{character}: {message}"
episode_messages.append(message)
yield episode_messages

48
toolbox/modules/soda_pdm.py
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import typing as t
from toolbox.core.consts import PromptConstants
from toolbox.datasets.soda import SodaDataset
from toolbox.modules import BaseModule
class SodaPDM(BaseModule):
'''Persona Dialogue Module based on the SODA dataset.'''
def generator(self) -> t.Generator[list[str], None, None]:
for episode in SodaDataset():
episode_messages = []
# NOTE(TG): We determine which order the speakers go on based on whether the relation is xAttr or not.
# This is because some speakers are more abstract concepts rather than concrete names,
# which would make them much more suitable as a bot
if episode.relation == "xAttr":
bot_name = episode.speakers[0]
user_name = episode.speakers[1]
else:
user_name = episode.speakers[0]
bot_name = episode.speakers[1]
# First, we would want to set the persona.
# However, the only acceptable description of a persona would be when episode.relation is "xAttr", since that directly describes
# a person in the conversation.
if episode.relation == "xAttr":
episode_messages.append(f"{PromptConstants.pdm_prefix_for(bot_name)}: {episode.literal}")
# Next, set the scenario.
# Make sure to replace any instance of the person representing the user in the conversation with the user token
replaced_narrative = episode.narrative.replace(user_name, PromptConstants.USER_TOKEN)
scenario = f"Scenario: {replaced_narrative}"
episode_messages.append(scenario)
# Next, the start token
episode_messages.append(PromptConstants.CHAT_START_TOKEN)
# I am going to assume that the length of episode.speakers is the same as the length of episode.dialogue
# Looked pretty clean to me in the data. Fuck it, TODO: account for the possibility of that happening
for i, utterance in enumerate(episode.dialogue):
# For now, just leave bot's name unreplaced.
if episode.speakers[i] == user_name:
name = PromptConstants.USER_PREFIX
else:
name = bot_name
episode_messages.append(f"{name}: {utterance.replace(user_name, PromptConstants.USER_TOKEN)}")
yield episode_messages

217
toolbox/scripts/build_dataset.py
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@ -1,217 +0,0 @@
#!/usr/bin/env python3
import argparse
import hashlib
import importlib
import json
import logging
import os
import random
import subprocess
import sys
import typing as t
from toolbox.core.consts import PromptConstants
from toolbox.modules import BaseModule
from toolbox.utils.strings import contains_suspect_unicode
# TODO(11b): Needs manual maintenance to keep up-to-date. Consider doing some
# metaprogramming trickery to build this list out instead.
DEFAULT_MODULE_LIST = [
"characterai_pdm:CharacterAiPDM",
# "discord_vdm:DiscordVDM",
# KajiwotoPDM has a bunch of garbage I need to filter, disabling in favor
# of the vanilla dialogue module for now.
# "kajiwoto_pdm:KajiwotoPDM",
# "kajiwoto_vdm:KajiwotoVDM",
# "light_dialogue_pdm:LightDialoguePDM",
]
DEFAULT_MODULES_STRING = ",".join(DEFAULT_MODULE_LIST)
def main() -> None:
random.seed(42)
parser = argparse.ArgumentParser()
parser.add_argument(
"-o",
"--output-name",
help="Path to write to. Should not include a file extension.")
parser.add_argument("-m",
"--modules",
default=DEFAULT_MODULES_STRING,
help="List of modules to use, comma-separated.")
parser.add_argument(
"-p",
"--print",
type=int,
help="If given, print this many episodes instead of writing to a file.")
parser.add_argument(
"-s",
"--skip",
type=int,
help="If given, skip over this many episodes before printing.")
parser.add_argument("-v",
"--verbose",
action="store_true",
help="Enable verbose logging.")
args = parser.parse_args()
logging.basicConfig(
format='[%(asctime)s] [%(levelname)s] %(message)s',
level=logging.DEBUG if args.verbose else logging.INFO,
)
# Sanity checks.
if args.output_name and args.print:
raise Exception("--output-name and --print are mutually exclusive.")
if args.skip and not args.print:
raise Exception("--skip can only be used in conjunction with --print.")
modules = _import_modules_from_string(args.modules)
#
# If the print argument was specified, print and exit.
#
if args.print:
idx = 0
episodes_to_skip = args.skip if args.skip is not None else None
for module in modules:
for episode in module():
if episodes_to_skip:
episodes_to_skip -= 1
continue
idx += 1
if idx > args.print:
sys.exit()
# Print a newline to visually separate different episodes.
if idx != 1:
print()
for ep in _episode_augmentations(episode):
print("---| New Episode |---")
print("---------------------")
print("\n---\n".join(ep + [PromptConstants.EOS_TOKEN]))
sys.exit()
#
# Otherwise, proceed with the writing logic.
#
# If no output name is given, we build one from the current git revision
# plus a hash of the given arguments. That way, the same dataset should
# theoretically always have the same output name, which is helpful for
# reproducibility and bailing out early (e.g. if the file already exists).
if args.output_name is None:
args_hash = hashlib.sha256(str(args).encode("utf-8")).hexdigest()[:7]
output_name = f"rev-{_get_git_revision_short_hash()}-args-{args_hash}"
else:
output_name = args.output_name
# Open the output file.
output_filename = f"{output_name}.jsonl"
if os.path.exists(output_filename):
raise Exception(f"{output_filename} already exists, aborting.")
with open(output_filename, "w", encoding="utf-8") as output_file:
# Iterate over each module sequentially, and write the data out into the
# file.
for module in modules:
for episode in module():
text = "\n".join(episode)
if contains_suspect_unicode(text):
print(
f"Skipping. Found suspect unicode contents in `{text}`")
continue
for augmented_episode in _episode_augmentations(episode):
text = "\n".join(augmented_episode +
[PromptConstants.EOS_TOKEN])
json_line = json.dumps({"text": text})
output_file.write(f"{json_line}\n")
#
# Helpers and CLI entrypoint.
#
def _episode_augmentations(
episode: list[str]) -> t.Generator[list[str], None, None]:
'''
Generates augmented data for the given episode.
The first 1.3B model had wildly unpredictable performance at the start of
conversations, which I attributed to the fact that originally we always fed
the model entire episodes to train on, so there were no examples of freshly
started conversations, in a sense.
This function takes a complete episode and yields different permutations of
it in an attempt to provide that data (e.g. with/without persona, with only
X messages in the history, X+2, X+4 and so on).
'''
permutated_episode = []
offset_idx = 0
# Don't discard the original episode.
yield episode
for turn in episode:
if "'s Persona: " in turn or "Scenario: " in turn or PromptConstants.CHAT_START_TOKEN in turn:
permutated_episode.append(turn.strip())
offset_idx += 1
continue
while len(episode) > 1 + offset_idx:
permutated_episode.append(episode.pop(offset_idx))
permutated_episode.append(episode.pop(offset_idx))
# Yielding every single instance results in too much data
# repetition, so instead we take a random sample.
should_yield = random.randint(0, 100) < 25
if should_yield:
yield permutated_episode
# Also, yield a version with _just_ dialogue if we've been yielding
# with persona/scenario data this entire time.
if offset_idx == 0:
continue
should_yield = random.randint(0, 100) < 25
if should_yield:
yield permutated_episode[offset_idx:]
def _get_git_revision_short_hash() -> str:
'''Returns the project's short git revision hash.'''
return subprocess.check_output(
["git", "rev-parse", "--short", "HEAD"],
cwd=os.path.join(os.path.dirname(os.path.realpath(__file__)), "..",
"..")).decode("ascii").strip()
def _import_modules_from_string(string: str) -> t.List[t.Type[BaseModule]]:
'''Imports all the module classes from the given, comma-separated string.'''
modules: t.List[t.Type[BaseModule]] = []
for module_and_class_name in string.split(","):
qualified_module_name = "toolbox.modules"
try:
module_name, class_name = module_and_class_name.split(":")
qualified_module_name = f"toolbox.modules.{module_name}"
except ValueError:
class_name = module_and_class_name
module = importlib.import_module(qualified_module_name)
modules.append(getattr(module, class_name))
return modules
if __name__ == "__main__":
main()

19
toolbox/utils/dataset.py
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import os
import typing as t
HERE = os.path.realpath(os.path.dirname(__file__))
def get_data_path(dataset_name: t.Optional[str] = None) -> str:
'''
Returns an absolute path to either the data folder, or a specific dataset if
`dataset_name` is supplied.
'''
if 'WAIFU_DATA_PATH' in os.environ:
return os.environ['WAIFU_DATA_PATH']
components = [HERE, "..", "..", "data"]
if dataset_name:
components.append(dataset_name)
return os.path.join(*components)

72
toolbox/utils/strings.py
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@ -1,72 +0,0 @@
'''Utility functions to clean up text strings.'''
# Some of this is pasta from Meta's ParlAI. See:
# https://github.com/facebookresearch/ParlAI/blob/main/parlai/utils/strings.py
import regex
def normalize_string(text: str, version: int = 1) -> str:
'''
Standardize the capitalization and punctuation spacing of the input text.
- Version 1: Fix sentence start casing and punctuation.
- Version 2: Add trailing period, if missing.
'''
switch_list = [(' .', '.'), (' ,', ','), (' ?', '?'), (' !', '!'),
(" ' ", "'")]
# add spaces so that words and punctuation can be seaprated
new_text = text.lower()
# normalize in case of human:
for new, old in switch_list:
new_text = new_text.replace(old, new).replace(' ', ' ')
# split on punctuation to find sentence boundaries
# capitalize stuff
tokens = new_text.split(' ')
for i in range(len(tokens)):
if i == 0:
tokens[i] = uppercase(tokens[i])
elif tokens[i] in ('i', "i'm", "i've", "i'll", "i'd"):
tokens[i] = uppercase(tokens[i])
elif tokens[i] in '?.!' and i < len(tokens) - 1:
tokens[i + 1] = uppercase(tokens[i + 1])
new_text = ' '.join(tokens)
new_text = ' ' + new_text + ' '
for tup in switch_list:
new_text = new_text.replace(tup[0], tup[1])
# get rid of surrounding whitespace
new_text = new_text.strip()
new_text = new_text.replace(' ', ' ')
if version > 1 and new_text and new_text[-1] not in '!.?)"\'':
new_text += '.'
return new_text
def title_case(string: str) -> str:
'''Converts a string into Title Case.'''
return " ".join([uppercase(word) for word in string.split(" ")])
def uppercase(string: str) -> str:
'''
Makes the first character of the string uppercase, if the string is
non-empty.
'''
if len(string) == 0:
return string
else:
return string[0].upper() + string[1:]
def contains_suspect_unicode(string: str) -> bool:
'''
Returns whether the given string seems to have suspect Unicode trickery
(e.g.: Zalgo text).
'''
return regex.search(r"\pM{3,}", string) is not None