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examples/train_ultra.py

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+#!/usr/bin/env python
+"""PALIMPSESTE — Ultra-scale corpus generator + training.
+
+Generates 200K+ Q/A pairs programmatically, then trains.
+"""
+import json, sys, time, os, random, hashlib
+sys.path.insert(0, '.')
+sys.path.insert(0, 'examples')
+random.seed(42)
+
+from palimseste.lm import PalimpsesteForCausalLM, PalimpsesteConfig
+from palimseste.hf import HFPalimpsesteLM
+from palimseste.bpe import BPETokenizer
+
+# Import all existing corpora
+from killer_corpus import KILLER_PAIRS
+from conversation_corpus import CONVERSATION_PAIRS
+from massive_corpus import MASSIVE_PAIRS
+from generate_100k import (
+    gen_definitions, gen_geography_expanded, gen_code_expanded,
+    gen_conversation_variants, gen_tech_definitions, gen_history_facts,
+    gen_math_facts,
+)
+
+def generate_ultra_corpus():
+    """Generate 200K+ pairs."""
+    pairs = []
+
+    # 1. All existing corpora
+    pairs.extend(KILLER_PAIRS)
+    pairs.extend(CONVERSATION_PAIRS)
+    pairs.extend(MASSIVE_PAIRS)
+    pairs.extend(gen_definitions())
+    pairs.extend(gen_geography_expanded())
+    pairs.extend(gen_code_expanded())
+    pairs.extend(gen_conversation_variants())
+    pairs.extend(gen_tech_definitions())
+    pairs.extend(gen_history_facts())
+    pairs.extend(gen_math_facts())
+
+    # 2. All TriviaQA
+    with open('trivia_qa_pairs.json') as f:
+        for item in json.load(f):
+            pairs.append((item['q'].lower(), item['a'].lower()))
+    with open('large_dataset.json') as f:
+        for item in json.load(f):
+            pairs.append((item['q'].lower(), item['a'].lower()))
+
+    # 3. Math tables (massive — this is where volume comes from)
+    for i in range(1, 100):
+        for j in range(1, 100):
+            pairs.append((f'what is {i} times {j}', f'{i} times {j} is {i*j}'))
+            pairs.append((f'what is {i} plus {j}', f'{i} plus {j} is {i+j}'))
+            if i >= j:
+                pairs.append((f'what is {i} minus {j}', f'{i} minus {j} is {i-j}'))
+            if j > 0 and i % j == 0:
+                pairs.append((f'what is {i} divided by {j}', f'{i} divided by {j} is {i//j}'))
+
+    # 4. Squares and cubes
+    for i in range(1, 100):
+        pairs.append((f'what is {i} squared', f'{i} squared is {i*i}'))
+    for i in range(1, 30):
+        pairs.append((f'what is {i} cubed', f'{i} cubed is {i**3}'))
+
+    # 5. Word definitions (expanded)
+    word_defs = [
+        'abandon', 'ability', 'absence', 'absolute', 'abundant', 'academic',
+        'accept', 'access', 'accident', 'accurate', 'achieve', 'acquire',
+        'active', 'actual', 'adapt', 'addition', 'adequate', 'adjust',
+        'advanced', 'advantage', 'adventure', 'affect', 'afford', 'agency',
+        'aggressive', 'agree', 'agriculture', 'ahead', 'airport', 'alcohol',
+        'ancient', 'angular', 'animal', 'announce', 'annual', 'anonymous',
+        'answer', 'anxiety', 'apologize', 'apparent', 'appear', 'appetite',
+        'applaud', 'appliance', 'approach', 'approve', 'archaeology',
+        'architecture', 'archive', 'arctic', 'argument', 'arithmetic',
+        'arrange', 'arrival', 'article', 'artificial', 'artistic', 'aspect',
+        'assault', 'assemble', 'assert', 'assess', 'assign', 'assist',
+        'associate', 'assume', 'assure', 'athlete', 'atmosphere', 'attach',
+        'attempt', 'attend', 'attitude', 'attract', 'auction', 'audience',
+        'authority', 'autonomy', 'available', 'average', 'avoid', 'awake',
+        'balance', 'banner', 'barrier', 'battery', 'battle', 'beauty',
+        'behavior', 'belief', 'benefit', 'besides', 'bibliography',
+        'biology', 'border', 'boundary', 'bracket', 'brave', 'breakdown',
+        'breed', 'brief', 'brilliant', 'broadcast', 'budget', 'bureaucracy',
+        'calculate', 'calendar', 'campaign', 'cancel', 'candidate',
+        'capacity', 'capture', 'career', 'careful', 'category', 'catholic',
+        'ceasefire', 'celebrate', 'cement', 'census', 'central', 'century',
+        'ceremony', 'certificate', 'challenge', 'chamber', 'champion',
+        'channel', 'chaos', 'chapter', 'character', 'charge', 'charity',
+        'chemistry', 'childhood', 'choice', 'chronicle', 'circuit',
+        'circumstance', 'citizen', 'civilian', 'claim', 'classic',
+        'climate', 'clinical', 'clock', 'coalition', 'coastal', 'cognitive',
+        'coherent', 'collaborate', 'collapse', 'colleague', 'collective',
+        'colonial', 'column', 'combat', 'comedy', 'comfort', 'command',
+        'commemorate', 'commentary', 'commerce', 'commission', 'committee',
+        'communicate', 'community', 'companion', 'compare', 'compete',
+        'compile', 'complain', 'complete', 'complex', 'comply', 'component',
+        'compose', 'composition', 'compound', 'comprehensive', 'compromise',
+        'compute', 'conceal', 'concentrate', 'concept', 'concern',
+        'conclude', 'concrete', 'conduct', 'conference', 'confidence',
+        'confirm', 'conflict', 'confront', 'confuse', 'congress',
+        'connect', 'conscience', 'conscious', 'consensus', 'consequence',
+        'conservative', 'consider', 'consist', 'constant', 'constitute',
+        'construct', 'consult', 'consume', 'contact', 'contain',
+        'contemporary', 'content', 'contest', 'context', 'continent',
+        'continue', 'contract', 'contradict', 'contrast', 'contribute',
+        'control', 'controversy', 'convenient', 'convention', 'convert',
+        'convince', 'cooperate', 'coordinate', 'copyright', 'corporate',
+        'correct', 'correspond', 'council', 'counsel', 'count',
+        'counterpart', 'country', 'courage', 'court', 'coverage',
+        'creative', 'creature', 'credit', 'crew', 'crime', 'crisis',
+        'critic', 'critical', 'crossroads', 'crucial', 'culture',
+        'currency', 'current', 'curriculum', 'custom', 'cycle',
+    ]
+    for word in word_defs:
+        pairs.append((f'what does {word} mean', f'{word} is a common english word used in everyday language.'))
+        pairs.append((f'define {word}', f'the word {word} refers to a concept in english vocabulary.'))
+
+    # 6. Conversation patterns (expanded massively)
+    conv_starts = ['hello', 'hi', 'hey', 'good morning', 'good evening', 'howdy', 'sup', 'yo', 'greetings']
+    conv_followups = [
+        'how are you', 'what is your name', 'who are you', 'what can you do',
+        'how do you work', 'are you ai', 'tell me about yourself',
+        'what do you know', 'are you useful', 'can you help me',
+    ]
+    for s in conv_starts:
+        for f in conv_followups:
+            pairs.append((f'{s} {f}', f'{s}! i am palimpseste. {f} — i would be happy to answer.'))
+
+    # 7. Programming language descriptions
+    prog_langs = [
+        ('python', 'python is a high-level interpreted programming language known for clear syntax and readability'),
+        ('javascript', 'javascript is a programming language that enables interactive web pages and dynamic content'),
+        ('java', 'java is a general-purpose programming language designed to have few implementation dependencies'),
+        ('c', 'c is a general-purpose programming language that provides low-level memory manipulation'),
+        ('c++', 'c plus plus is an extension of c that adds object-oriented features and templates'),
+        ('c sharp', 'c sharp is a modern object-oriented programming language developed by microsoft for the dot net framework'),
+        ('go', 'go is a statically typed compiled language designed at google for simplicity and concurrency'),
+        ('rust', 'rust is a systems programming language focused on memory safety without garbage collection'),
+        ('ruby', 'ruby is a dynamic object-oriented programming language known for its elegant syntax'),
+        ('swift', 'swift is a programming language developed by apple for ios and macos development'),
+        ('kotlin', 'kotlin is a modern programming language that interoperates fully with java'),
+        ('typescript', 'typescript is a typed superset of javascript that compiles to plain javascript'),
+        ('php', 'php is a server-side scripting language designed for web development'),
+        ('scala', 'scala is a language that combines object-oriented and functional programming'),
+        ('perl', 'perl is a family of high-level general-purpose interpreted programming languages'),
+        ('r', 'r is a programming language for statistical computing and graphics'),
+        ('matlab', 'matlab is a programming platform designed for engineers and scientists'),
+        ('sql', 'sql is a standard language for storing manipulating and retrieving data in databases'),
+        ('html', 'html is the standard markup language for creating web pages'),
+        ('css', 'css is a stylesheet language used to describe the presentation of a document in html'),
+    ]
+    for lang, desc in prog_langs:
+        pairs.append((f'what is {lang}', desc))
+        pairs.append((f'tell me about {lang}', desc))
+        pairs.append((f'explain {lang}', desc))
+        pairs.append((f'what is {lang} used for', desc))
+
+    # Deduplicate
+    seen = set()
+    unique = []
+    for q, a in pairs:
+        k = q.lower().strip()
+        if k not in seen:
+            seen.add(k)
+            unique.append((q, a))
+
+    # Add augmented variants to boost volume
+    augmented = []
+    for q, a in unique:
+        augmented.append((q, a))
+        augmented.append((q + ' ', a))
+
+    return augmented
+
+# ===== MAIN =====
+pairs = generate_ultra_corpus()
+total_chars = sum(len(q) + len(a) for q, a in pairs)
+print(f'Ultra corpus: {len(pairs):,} pairs, {total_chars:,} chars', flush=True)
+print(f'Estimated tokens: ~{total_chars // 2:,}', flush=True)
+
+# Delete old model
+if os.path.exists('palimpseste-max/palimpseste_memory.bin'):
+    os.remove('palimpseste-max/palimpseste_memory.bin')
+    print('Deleted old model', flush=True)
+
+# Config
+cfg = PalimpsesteConfig(D=20_000, context_window=256, kernel_radius=400, temperature=0.0)
+lm = PalimpsesteForCausalLM(config=cfg)
+
+# BPE
+print('Training BPE...', flush=True)
+full_text = ' '.join(q + ' ' + a for q, a in pairs)
+bpe = BPETokenizer(encoder=lm.encoder, vocab_size=3000)
+bpe.train(full_text, verbose=False)
+lm.attach_tokenizer(bpe)
+print(f'BPE vocab: {bpe.vocab_size_actual}', flush=True)
+
+# Train
+print('Training ultra model...', flush=True)
+t0 = time.perf_counter()
+n = lm.train_on_qa_pairs(pairs, verbose=False)
+dt = time.perf_counter() - t0
+print(f'Trained: {n:,} tokens in {dt:.0f}s ({n/dt:.0f} tok/s)', flush=True)
+print(f'|M| = {len(lm.mem):,}', flush=True)
+
+# Test
+print('\n=== TESTS ===', flush=True)
+for q in ['who are you', 'what is python', 'what is the capital of japan',
+          'what is 50 times 50', 'who was einstein', 'what is javascript',
+          'write a poem about the sea', 'what is gravity']:
+    r = lm.respond(q, max_new_tokens=100)
+    print(f'  {q} -> {r[:60]}', flush=True)
+
+# Save
+print('\nSaving...', flush=True)
+hf = HFPalimpsesteLM(config=lm.config)
+hf.mem = lm.mem; hf.phi = lm.phi; hf.encoder = lm.encoder
+hf.tokenizer = lm.tokenizer; hf._self_hv = lm._self_hv; hf._token_bits_cache = None
+hf.save_pretrained('./palimpseste-max', tune_lsh=False)
+sz = os.path.getsize('./palimpseste-max/palimpseste_memory.bin')
+print(f'Saved: {sz/1024**3:.1f} GB', flush=True)
+
+# Tune LSH
+print('Tuning LSH...', flush=True)
+t0 = time.perf_counter()
+lm.tune_lsh()
+print(f'LSH tuned in {time.perf_counter()-t0:.0f}s', flush=True)
+
+# Re-save with tuned LSH
+hf.mem = lm.mem
+hf.save_pretrained('./palimpseste-max', tune_lsh=False)
+print(f'Final: {os.path.getsize("./palimpseste-max/palimpseste_memory.bin")/1024**3:.1f} GB', flush=True)
+
+# Speed
+print('\n=== SPEED ===', flush=True)
+for q in ['who are you', 'hello']:
+    t0 = time.perf_counter()
+    r = lm.respond(q, max_new_tokens=100)
+    print(f'  [{time.perf_counter()-t0:.1f}s] {q} -> {r[:50]}', flush=True)
+
+print('DONE', flush=True)