diff --git a/config.py b/config.py deleted file mode 100644 index 1f31c4d..0000000 --- a/config.py +++ /dev/null @@ -1,19 +0,0 @@ -import os -import torch -from dotenv import load_dotenv - -load_dotenv() - - -class Config: - model_dim = int(os.getenv("MODEL_DIM", 256)) - num_layers = int(os.getenv("NUM_LAYERS", 4)) - num_heads = int(os.getenv("HEADS", 8)) - vocab_size = int(os.getenv("VOCAB_SIZE", 30000)) - context_size = int(os.getenv("CONTEXT_SIZE", 512)) - batch_size = int(os.getenv("BATCH_SIZE", 8)) - lr = float(os.getenv("LEARNING_RATE", 1e-4)) - device = "cuda" if torch.cuda.is_available() else "cpu" - - -cfg = Config() diff --git a/dashboard.py b/dashboard.py new file mode 100644 index 0000000..51718af --- /dev/null +++ b/dashboard.py @@ -0,0 +1,25 @@ +from flask import Flask, render_template_string +from debug import DebugMonitor + +debug = DebugMonitor() + +app = Flask(__name__) + + +@app.route("/") +def home(): + return render_template_string(""" + + Ruby Debug Dashboard + +

🧠 Ruby Live Debug

+

Last Dream: {{ debug.last_dream }}

+

Last Thought: {{ debug.last_thought }}

+

Last Loss: {{ debug.last_loss }}

+

Last Reply: {{ debug.last_context }}

+ + + """, debug=debug) + +if __name__ == "__main__": + app.run(port=5000) diff --git a/debug.py b/debug.py new file mode 100644 index 0000000..c07dd39 --- /dev/null +++ b/debug.py @@ -0,0 +1,29 @@ +from datetime import datetime + + +class DebugMonitor: + def __init__(self): + self.last_dream = "" + self.last_thought = "" + self.last_loss = 0.0 + self.last_context = "" + + def log_dream(self, dream): + self.last_dream = dream + self._print("💤 Dream", dream) + + def log_thought(self, thought): + self.last_thought = thought + self._print("💭 Thought", thought) + + def log_loss(self, loss): + self.last_loss = loss + self._print("📉 Loss", f"{loss:.4f}") + + def log_context(self, context): + self.last_context = context + self._print("📖 Context", context) + + def _print(self, label, content): + now = datetime.now().strftime("%H:%M:%S") + print(f"[{now}] {label}: {content}") diff --git a/dream.py b/dream.py new file mode 100644 index 0000000..a0906ec --- /dev/null +++ b/dream.py @@ -0,0 +1,29 @@ +import torch +import time +from utils import sample_reply, update_model_vocab +from debug import DebugMonitor + +debug = DebugMonitor() + + +def run_dream_loop(model, tokenizer, device, optimizer, train_step, interval=120): + print("Ruby is dreaming...") + while True: + reply, loss = generate_dream(model, tokenizer, device, optimizer, train_step) + print(f"[DREAM] {reply} (loss={loss:.4f})") + time.sleep(interval) + + +def generate_dream(model, tokenizer, device, optimizer, train_step): + update_model_vocab(model, tokenizer) + + prompt = "Ruby: " + input_ids = tokenizer.encode(prompt, return_tensors=True, freeze=True).to(device) + + reply = sample_reply(model, tokenizer, input_ids) + training_text = f"User: What do you think?\nRuby: {reply}" + + loss = train_step(model, optimizer, tokenizer, training_text, device) + return reply, loss + debug.log_dream(reply) + debug.log_loss(loss) diff --git a/feedback.py b/feedback.py new file mode 100644 index 0000000..53434e5 --- /dev/null +++ b/feedback.py @@ -0,0 +1,4 @@ +def basic_self_feedback(reply, user_response): + if user_response and len(user_response.strip()) > 1: + return 1.0 + return -0.5 diff --git a/memory.py b/memory.py new file mode 100644 index 0000000..b3e120e --- /dev/null +++ b/memory.py @@ -0,0 +1,29 @@ +import json +from pathlib import Path + + +class MemoryBuffer: + def __init__(self, max_len=3, path="memory.json"): + self.path = Path(path) + self.max_len = max_len + self.memory = [] + self.load() + + def add(self, user_input, bot_reply): + self.memory.append(f"User: {user_input}") + self.memory.append(f"Bot: {bot_reply}") + if len(self.memory) > self.max_len * 2: + self.memory = self.memory[-self.max_len * 2:] + self.save() + + def get_context(self): + return self.memory.copy() + + def save(self): + with open(self.path, "w", encoding="utf-8") as f: + json.dump(self.memory, f) + + def load(self): + if self.path.exists(): + with open(self.path, "r", encoding="utf-8") as f: + self.memory = json.load(f) diff --git a/model.py b/model.py new file mode 100644 index 0000000..3c0ec1a --- /dev/null +++ b/model.py @@ -0,0 +1,30 @@ +import torch +import torch.nn as nn + + +class MiniTransformer(nn.Module): + def __init__(self, + vocab_size, + d_model=256, + n_heads=4, + n_layers=4, + max_seq_len=512): + super().__init__() + self.token_emb = nn.Embedding(vocab_size, d_model) + self.pos_emb = nn.Parameter(torch.zeros(1, max_seq_len, d_model)) + self.layers = nn.ModuleList([ + nn.TransformerEncoderLayer(d_model=d_model, + nhead=n_heads, + batch_first=True) + for _ in range(n_layers) + ]) + self.ln = nn.LayerNorm(d_model) + self.head = nn.Linear(d_model, vocab_size) + + def forward(self, x): + B, T = x.size() + x = self.token_emb(x) + self.pos_emb[:, :T] + for layer in self.layers: + x = layer(x) + x = self.ln(x) + return self.head(x) diff --git a/personality.py b/personality.py new file mode 100644 index 0000000..c303a66 --- /dev/null +++ b/personality.py @@ -0,0 +1,70 @@ +import json +from pathlib import Path +import random + + +class Personality: + def __init__(self, path="personality.json"): + self.path = Path(path) + self.data = { + "likes": [], + "dislikes": [], + "traits": [], + "curiosities": [] + } + self.load() + + def load(self): + if self.path.exists(): + with open(self.path, "r", encoding="utf-8") as f: + self.data.update(json.load(f)) + + def save(self): + with open(self.path, "w", encoding="utf-8") as f: + json.dump(self.data, f, indent=2) + + def learn_topic(self, text): + words = [w.lower() for w in text.split()] + for word in words: + if word.isalpha() and word not in self.data["curiosities"]: + self.data["curiosities"].append(word) + self.save() + + def choose_curiosity(self): + if not self.data["curiosities"]: + return None + return random.choice(self.data["curiosities"]) + + def observe_input(self, message: str): + text = message.lower() + + # Learn likes + if "i like" in text: + word = text.split("i like", 1)[1].strip().split()[0] + if word and word not in self.data["likes"]: + self.data["likes"].append(word) + self.save() + + # Learn dislikes + if "i hate" in text or "i don't like" in text: + for phrase in ["i hate", "i don't like"]: + if phrase in text: + word = text.split(phrase, 1)[1].strip().split()[0] + if word and word not in self.data["dislikes"]: + self.data["dislikes"].append(word) + self.save() + + # Learn traits from compliments + for trigger in ["you are", "you're", "ur"]: + if trigger in text: + fragment = text.split(trigger, 1)[1].strip().split()[0] + if fragment and fragment not in self.data["traits"]: + self.data["traits"].append(fragment) + self.save() + + def reflect(self) -> str: + if not self.data["likes"] and not self.data["traits"]: + return "I'm still figuring out who I am." + likes = ', '.join(self.data["likes"][:3]) or "nothing yet" + traits = ', '.join(self.data["traits"][:3]) or "no traits yet" + return f"I'm starting to think I like {likes}. People have called me {traits}." diff --git a/ruby.py b/ruby.py new file mode 100644 index 0000000..333bb62 --- /dev/null +++ b/ruby.py @@ -0,0 +1,106 @@ +import discord +import torch +from debug import DebugMonitor +from dream import run_dream_loop +from model import MiniTransformer +from train_step import online_train_step +from tokenizer import ChildTokenizer +from feedback import basic_self_feedback +from memory import MemoryBuffer +from utils import update_model_vocab, track_loss, sample_reply, sample_thought +from personality import Personality +from dotenv import load_dotenv +import os +import logging +import threading + + +# Configure logging +logging.basicConfig(filename='ruby.log', level=logging.ERROR) + +# Load environment variables +load_dotenv() +TOKEN = os.getenv('DISCORD_TOKEN') + +# Initialize personality +personality = Personality() + +# Initialize debug monitor +debug = DebugMonitor() + +# Initialize model +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +tokenizer = ChildTokenizer() +memory = MemoryBuffer(max_len=3) + +model = MiniTransformer(vocab_size=tokenizer.vocab_size()).to(device) +optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) + +# Initialize Discord client +intents = discord.Intents.default() +intents.message_content = True +client = discord.Client(intents=intents) + +# Start the dream loop in a separate thread +dream_thread = threading.Thread( + target=run_dream_loop, + args=(model, tokenizer, device, optimizer, online_train_step), + daemon=True +) +dream_thread.start() + + +# Event handlers +@client.event +async def on_ready(): + print(f"{client.user} is ready and learning!") + + +@client.event +async def on_message(message): + try: + # Ignore bot's own messages + if message.author == client.user: + return + + # Get user input and memory + user_input = message.content + context = memory.get_context() + full_input = ' '.join(context + [user_input]) + + # 🔍 Debug: log context + debug.log_context(full_input) + + # Ensure model matches tokenizer + update_model_vocab(model, tokenizer) + + # Encode user input + input_ids = tokenizer.encode(full_input, return_tensors=True, freeze=True).to(device) + if input_ids.size(1) < 2: + return + + # 💭 Generate internal thought + thought = sample_thought(model, tokenizer, device, full_input) + debug.log_thought(thought) + + # 🗣️ Generate reply from Ruby + reply = sample_reply(model, tokenizer, input_ids) + debug.log_context(reply) + + # ✅ Send the reply + await message.channel.send(reply if reply.strip() else "...") + + # Add to memory + memory.add(user_input, reply) + + # 📉 Train and log loss + training_example = f"User: {user_input}\nRuby: {reply}" + loss = online_train_step(model, optimizer, tokenizer, training_example, device) + debug.log_loss(loss) + + except Exception as e: + logging.exception("Error in on_message") + await message.channel.send("Oops, I had a brain freeze.") + + +client.run(TOKEN) diff --git a/tokenizer.json b/tokenizer.json deleted file mode 100644 index 0923e5e..0000000 --- a/tokenizer.json +++ /dev/null @@ -1,107920 +0,0 @@ -{ - "version": "1.0", - "truncation": null, - "padding": null, - "added_tokens": [ - { - "id": 0, - "content": "[PAD]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - }, - { - "id": 1, - "content": "[UNK]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - } - ], - "normalizer": null, - "pre_tokenizer": null, - "post_processor": null, - "decoder": { - "type": "ByteLevel", - "add_prefix_space": true, - "trim_offsets": true, - "use_regex": true - }, - "model": { - "type": "BPE", - "dropout": null, - "unk_token": "[UNK]", - "continuing_subword_prefix": null, - "end_of_word_suffix": null, - "fuse_unk": false, - "byte_fallback": false, - "ignore_merges": false, - "vocab": { - "[PAD]": 0, - "[UNK]": 1, - "!": 2, - "\"": 3, - "#": 4, - "$": 5, - "%": 6, - "&": 7, - "'": 8, - "(": 9, - ")": 10, - "*": 11, - "+": 12, - ",": 13, - "-": 14, - ".": 15, - "/": 16, - "0": 17, - "1": 18, - "2": 19, - 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"ff" - ], - [ - "ang", - "les" - ], - [ - "record", - "ings" - ], - [ - "ium", - "." - ], - [ - "Glas", - "g" - ], - [ - "_", - "t" - ], - [ - "ro", - "," - ], - [ - "am", - "bers" - ], - [ - "ver", - "ified" - ], - [ - "sk", - "i," - ], - [ - "log", - "ging" - ], - [ - "Fran", - "co" - ] - ] - } -} \ No newline at end of file diff --git a/tokenizer.py b/tokenizer.py new file mode 100644 index 0000000..6b30b30 --- /dev/null +++ b/tokenizer.py @@ -0,0 +1,42 @@ +import torch +import logging + +logger = logging.getLogger("tokenizer") +logger.setLevel(logging.INFO) + +fh = logging.FileHandler("learned_chars.log") +formatter = logging.Formatter('%(message)s') +fh.setFormatter(formatter) +logger.addHandler(fh) + + +class ChildTokenizer: + def __init__(self): + self.char_to_id = {'': 0, '': 1} + self.id_to_char = {0: '', 1: ''} + self.next_id = 2 + + # 🔤 Bootstrap with common characters + for ch in "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ.,!? ':;": + self.char_to_id[ch] = self.next_id + self.id_to_char[self.next_id] = ch + self.next_id += 1 + + def encode(self, text, return_tensors=False, freeze=False): + ids = [] + for ch in text: + if ch not in self.char_to_id: + if freeze: + ids.append(self.char_to_id.get('', 1)) + continue + self.char_to_id[ch] = self.next_id + self.id_to_char[self.next_id] = ch + self.next_id += 1 + ids.append(self.char_to_id[ch]) + return torch.tensor([ids], dtype=torch.long) if return_tensors else ids + + def decode(self, ids): + return ''.join([self.id_to_char.get(i, '') for i in ids]) + + def vocab_size(self): + return self.next_id diff --git a/train.py b/train.py deleted file mode 100644 index a5d1bea..0000000 --- a/train.py +++ /dev/null @@ -1,159 +0,0 @@ -import torch -import torch.nn as nn -import time -import os -import numpy as np -from torch.utils.data import Dataset, DataLoader -from datasets import load_dataset -from tokenizers import Tokenizer, models, trainers, decoders -from config import cfg -from torch.cuda.amp import autocast, GradScaler - - -# 1. Tokenizer Implementation (Modified) -class RubyTokenizer: - def __init__(self): - self.tokenizer = Tokenizer(models.BPE(unk_token="[UNK]")) - self.tokenizer.add_special_tokens(["[PAD]", "[UNK]"]) - self.tokenizer.decoder = decoders.ByteLevel() - - def train(self, texts): - trainer = trainers.BpeTrainer( - special_tokens=["[PAD]", "[UNK]"], - vocab_size=cfg.vocab_size, - min_frequency=2, # Modified - show_progress=True - ) - self.tokenizer.train_from_iterator( - (text.split() for text in texts), # Modified: better word handling - trainer=trainer - ) - - def encode(self, text): - return self.tokenizer.encode(text).ids - - @property - def pad_id(self): - return self.tokenizer.token_to_id("[PAD]") # Modified - - -# 2. Optimized Dataset (Modified padding handling) -class CachedDataset(Dataset): - def __init__(self): - self.data = np.memmap("dataset_cache.bin", - dtype=np.int32, - mode="r", - shape=(os.path.getsize("dataset_cache.bin")//4,)) - - def __len__(self): - return len(self.data) // cfg.context_size - - def __getitem__(self, idx): - start = idx * cfg.context_size - return torch.from_numpy(self.data[start:start+cfg.context_size].copy()) - - -# 3. Transformer Model (Modified padding_idx) -class Transformer(nn.Module): - def __init__(self, pad_id): - super().__init__() - self.embed = nn.Embedding( - cfg.vocab_size, - cfg.model_dim, - padding_idx=pad_id # Modified - ) - self.blocks = nn.ModuleList([ - nn.TransformerEncoderLayer( - d_model=cfg.model_dim, - nhead=cfg.num_heads, - dim_feedforward=cfg.model_dim*4, - batch_first=True - ) for _ in range(cfg.num_layers) - ]) - self.head = nn.Linear(cfg.model_dim, cfg.vocab_size) - - def forward(self, x): - x = self.embed(x) - for block in self.blocks: - x = block(x) - return self.head(x) - - -# 4. Main Training Process (Critical fixes) -def main(): - # Initialize tokenizer - tokenizer = RubyTokenizer() - - if not os.path.exists("dataset_cache.bin"): - print("Creating dataset cache...") - ds = load_dataset("openwebtext", split="train[:5%]") - - # Train and save tokenizer (Modified) - if not os.path.exists("tokenizer.json"): - print("Training tokenizer...") - tokenizer.train([text for text in ds["text"] if len(text) > 100]) - tokenizer.tokenizer.save("tokenizer.json") - else: - tokenizer.tokenizer = Tokenizer.from_file("tokenizer.json") - - # Tokenize and cache data (Modified) - all_tokens = [] - pad_id = tokenizer.pad_id - - for text in ds["text"]: - tokens = tokenizer.encode(text) - tokens = tokens[:cfg.context_size] # Truncate after tokenization - pad_len = cfg.context_size - len(tokens) - all_tokens.extend(tokens + [pad_id]*pad_len) # Modified - - memmap = np.memmap("dataset_cache.bin", - dtype=np.int32, - mode="w+", - shape=(len(all_tokens),)) - memmap[:] = np.array(all_tokens, dtype=np.int32) - del memmap - - # Test tokenizer (Modified) - test_text = "The quick brown fox jumps over the lazy dog." - print("Tokenizer test:", tokenizer.tokenizer.encode(test_text).tokens) - - # Initialize model with pad_id (Modified) - model = Transformer(pad_id=tokenizer.pad_id).to(cfg.device) - opt = torch.optim.AdamW(model.parameters(), lr=cfg.lr) - scaler = GradScaler() - - dataset = CachedDataset() - loader = DataLoader(dataset, - batch_size=cfg.batch_size, - pin_memory=True, - shuffle=True) - - # Training loop (Modified loss calculation) - start = time.time() - for step, batch in enumerate(loader): - batch = batch.to(cfg.device, non_blocking=True) - - inputs = batch[:, :-1] - targets = batch[:, 1:] - - with autocast(): - outputs = model(inputs) - loss = torch.nn.functional.cross_entropy( - outputs.reshape(-1, cfg.vocab_size), - targets.reshape(-1).long(), - ignore_index=tokenizer.pad_id # Modified - ) - - scaler.scale(loss).backward() - scaler.step(opt) - scaler.update() - opt.zero_grad() - - if step % 10 == 0: - elapsed = time.time() - start - speed = (step + 1) * cfg.batch_size / elapsed - print(f"Step {step} | Loss: {loss.item():.4f} | Speed: {speed:.1f} samples/s") - - -if __name__ == "__main__": - main() diff --git a/train_step.py b/train_step.py new file mode 100644 index 0000000..c4b0867 --- /dev/null +++ b/train_step.py @@ -0,0 +1,34 @@ +import torch +import torch.nn.functional as F +from utils import update_model_vocab + + +def online_train_step(model, optimizer, tokenizer, message, device): + # Ensure model can handle current vocab + update_model_vocab(model, tokenizer) + + # Freeze tokenizer so it doesn't grow mid-train + tokens = tokenizer.encode(message, return_tensors=True, freeze=True).to(device) + if tokens.size(1) < 2: + return 0.0 + + # Truncate long input + max_len = model.pos_emb.size(1) + if tokens.size(1) > max_len: + tokens = tokens[:, -max_len:] + + x = tokens[:, :-1] + y = tokens[:, 1:] + + # HARD STOP if y exceeds model vocab + vocab_size = model.token_emb.num_embeddings + assert y.max().item() < vocab_size, f"y contains token > vocab_size ({y.max().item()} >= {vocab_size})" + + logits = model(x) + loss = F.cross_entropy(logits.view(-1, logits.size(-1)), y.view(-1)) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + return loss.item() diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..193c082 --- /dev/null +++ b/utils.py @@ -0,0 +1,86 @@ +import matplotlib.pyplot as plt +import torch +from torch.nn.functional import softmax + +loss_log = [] + + +def track_loss(loss): + loss_log.append(loss) + if len(loss_log) % 50 == 0: + plot_loss() + + +def plot_loss(): + plt.figure() + plt.plot(loss_log) + plt.title("Training Loss Over Time") + plt.xlabel("Steps") + plt.ylabel("Loss") + plt.savefig("loss_plot.png") + plt.close() + + +def update_model_vocab(model, tokenizer): + new_vocab = tokenizer.vocab_size() + old_vocab = model.token_emb.num_embeddings + d_model = model.token_emb.embedding_dim + + if new_vocab > old_vocab: + # Resize token embedding + old_weights = model.token_emb.weight.data + new_emb = torch.nn.Embedding(new_vocab, d_model).to(old_weights.device) + new_emb.weight.data[:old_vocab] = old_weights + torch.nn.init.normal_(new_emb.weight.data[old_vocab:], mean=0.0, std=0.02) + model.token_emb = new_emb + + # Resize output head + old_head = model.head + new_head = torch.nn.Linear(d_model, new_vocab).to(old_weights.device) + new_head.weight.data[:old_vocab] = old_head.weight.data + new_head.bias.data[:old_vocab] = old_head.bias.data + torch.nn.init.normal_(new_head.weight.data[old_vocab:], mean=0.0, std=0.02) + torch.nn.init.zeros_(new_head.bias.data[old_vocab:]) + model.head = new_head + + +def sample_reply(model, tokenizer, input_ids, max_len=40): + model.eval() + generated = input_ids.clone() + device = input_ids.device + + for _ in range(max_len): + # Truncate input to fit positional embedding + max_seq = model.pos_emb.size(1) + if generated.size(1) > max_seq: + generated = generated[:, -max_seq:] + + update_model_vocab(model, tokenizer) + + try: + logits = model(generated) + except RuntimeError as e: + print("CUDA crash in sample_reply — possible vocab mismatch") + print("Generated:", generated) + raise e + + next_token_logits = logits[0, -1, :] + probs = torch.nn.functional.softmax(next_token_logits, dim=-1) + next_token = probs.argmax(dim=-1, keepdim=True) + next_token = next_token.unsqueeze(0) # Shape: [1, 1] + generated = torch.cat((generated, next_token), dim=1) + + decoded = tokenizer.decode([next_token.item()]) + if decoded in ['\n', '.', '!', '?']: + break + + output = generated[0].tolist()[input_ids.shape[1]:] + reply = tokenizer.decode(output).strip() + print(f"[Reply] {repr(reply)}") + return reply + + +def sample_thought(model, tokenizer, device, context_text, max_len=60): + prompt = f"[thinking] {context_text}" + input_ids = tokenizer.encode(prompt, return_tensors=True).to(device) + return sample_reply(model, tokenizer, input_ids, max_len=max_len)