From 6f28a30268da5a77c61250bbfc9767bc61e81dcb Mon Sep 17 00:00:00 2001
From: Dan <daniel.sapelli@gmail.com>
Date: Tue, 8 Apr 2025 19:52:01 -0400
Subject: [PATCH] Attempting to create a growing AI
---
config.py | 19 -
dashboard.py | 25 +
debug.py | 29 +
dream.py | 29 +
feedback.py | 4 +
memory.py | 29 +
model.py | 30 +
personality.py | 70 +
ruby.py | 106 +
tokenizer.json | 107920 ----------------------------------------------
tokenizer.py | 42 +
train.py | 159 -
train_step.py | 34 +
utils.py | 86 +
14 files changed, 484 insertions(+), 108098 deletions(-)
delete mode 100644 config.py
create mode 100644 dashboard.py
create mode 100644 debug.py
create mode 100644 dream.py
create mode 100644 feedback.py
create mode 100644 memory.py
create mode 100644 model.py
create mode 100644 personality.py
create mode 100644 ruby.py
delete mode 100644 tokenizer.json
create mode 100644 tokenizer.py
delete mode 100644 train.py
create mode 100644 train_step.py
create mode 100644 utils.py
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("""
+ <html>
+ <head><title>Ruby Debug Dashboard</title></head>
+ <body>
+ <h1>🧠 Ruby Live Debug</h1>
+ <p><b>Last Dream:</b> {{ debug.last_dream }}</p>
+ <p><b>Last Thought:</b> {{ debug.last_thought }}</p>
+ <p><b>Last Loss:</b> {{ debug.last_loss }}</p>
+ <p><b>Last Reply:</b> {{ debug.last_context }}</p>
+ </body>
+ </html>
+ """, 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,
- "3": 20,
- "4": 21,
- "5": 22,
- "6": 23,
- "7": 24,
- "8": 25,
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- "<": 29,
- "=": 30,
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- "@": 33,
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- "B": 35,
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- "D": 37,
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- "H": 41,
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- "transl",
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- "S",
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- "price",
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- "pan",
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- "augh",
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- "wild",
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- "imag",
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- "(",
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- "per",
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- "ens",
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- "ref",
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- "can",
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- "gr",
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- "letter",
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- "un",
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- "software",
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- "children",
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- "c",
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- "al",
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- "plan",
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- "success",
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- "Ne",
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- "motor",
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- "party",
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- "los",
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- "repe",
- "ating"
- ],
- [
- "constitut",
- "es"
- ],
- [
- "M",
- "ist"
- ],
- [
- "y",
- "es."
- ],
- [
- "comp",
- "uls"
- ],
- [
- "dis",
- "pers"
- ],
- [
- "come",
- ","
- ],
- [
- "return",
- ","
- ],
- [
- "pris",
- "ing"
- ],
- [
- "complex",
- ","
- ],
- [
- "cool",
- "."
- ],
- [
- "!!!!!!!!",
- "!!!!!!!!"
- ],
- [
- "8",
- ",000"
- ],
- [
- ".[",
- "4"
- ],
- [
- "Red",
- "skins"
- ],
- [
- "February",
- "."
- ],
- [
- "B",
- "is"
- ],
- [
- "crim",
- "es."
- ],
- [
- "uk",
- "i"
- ],
- [
- "opt",
- "ion,"
- ],
- [
- "right",
- ")"
- ],
- [
- "trans",
- "fers"
- ],
- [
- "2.",
- "8"
- ],
- [
- "ling",
- "ton"
- ],
- [
- "lock",
- "s"
- ],
- [
- "document",
- "s."
- ],
- [
- "Alz",
- "heim"
- ],
- [
- "make",
- "up"
- ],
- [
- "pack",
- "aging"
- ],
- [
- "conce",
- "aled"
- ],
- [
- "Mir",
- "ror"
- ],
- [
- "(",
- "left"
- ],
- [
- "D",
- "ot"
- ],
- [
- "r",
- "ation."
- ],
- [
- "forum",
- "s"
- ],
- [
- "veget",
- "ables"
- ],
- [
- "M",
- "AT"
- ],
- [
- "br",
- "ag"
- ],
- [
- "Dr",
- "af"
- ],
- [
- "football",
- ","
- ],
- [
- "(",
- "Image:"
- ],
- [
- "way",
- ":"
- ],
- [
- "them",
- ".\""
- ],
- [
- "No",
- "va"
- ],
- [
- "milit",
- "ar"
- ],
- [
- "fre",
- "ed"
- ],
- [
- "Dis",
- "covery"
- ],
- [
- "out",
- "look"
- ],
- [
- "cas",
- "ino"
- ],
- [
- "Whats",
- "App"
- ],
- [
- ":",
- ")"
- ],
- [
- "“",
- "Do"
- ],
- [
- "Ad",
- "venture"
- ],
- [
- "Sch",
- "ult"
- ],
- [
- "internet",
- "."
- ],
- [
- "maxim",
- "ize"
- ],
- [
- "compat",
- "ibility"
- ],
- [
- "feas",
- "ible"
- ],
- [
- "w",
- "anna"
- ],
- [
- "make",
- "."
- ],
- [
- "Rock",
- "et"
- ],
- [
- "Sho",
- "ck"
- ],
- [
- "ste",
- "ering"
- ],
- [
- "boast",
- "s"
- ],
- [
- "с",
- "т"
- ],
- [
- "po",
- "ised"
- ],
- [
- "In",
- "spe"
- ],
- [
- "mar",
- "sh"
- ],
- [
- "E",
- "volution"
- ],
- [
- "G",
- "RE"
- ],
- [
- "in",
- "test"
- ],
- [
- "ol",
- "ig"
- ],
- [
- "hair",
- ","
- ],
- [
- "Sub",
- "aru"
- ],
- [
- "Fam",
- "il"
- ],
- [
- "N",
- "an"
- ],
- [
- "ot",
- "er"
- ],
- [
- "Hero",
- "es"
- ],
- [
- "(c",
- ")"
- ],
- [
- "G",
- "ut"
- ],
- [
- "sh",
- "i"
- ],
- [
- "ning",
- "s"
- ],
- [
- "partic",
- "le"
- ],
- [
- "president",
- "'s"
- ],
- [
- "kind",
- "a"
- ],
- [
- "app",
- "arat"
- ],
- [
- "bre",
- "wing"
- ],
- [
- "zomb",
- "ie"
- ],
- [
- "ro",
- "pe"
- ],
- [
- "dis",
- "placed"
- ],
- [
- "US",
- "E"
- ],
- [
- "ecolog",
- "ical"
- ],
- [
- "ific",
- "ation,"
- ],
- [
- "greet",
- "ed"
- ],
- [
- "s",
- "way"
- ],
- [
- "deb",
- "t,"
- ],
- [
- "hom",
- "ophob"
- ],
- [
- "Google",
- "+"
- ],
- [
- "request",
- "."
- ],
- [
- "Church",
- ","
- ],
- [
- "Mun",
- "ich"
- ],
- [
- "pon",
- "der"
- ],
- [
- "oper",
- "ative"
- ],
- [
- "$1",
- ",000"
- ],
- [
- "ine",
- "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 = {'<pad>': 0, '<unk>': 1}
+ self.id_to_char = {0: '<pad>', 1: '<unk>'}
+ 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('<unk>', 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, '<unk>') 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)