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d8bc908 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 | import os
import sys
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
import torch
import torch.nn.functional as F
import urllib.request
import sys
import math
from collections import Counter
from trigram import (
VOCAB, EMBEDDING_DIM, HIDDEN_DIM, FFN_HIDDEN, CTX, THRESHOLD,
SPECIAL_VOCAB, MORPHTernaryModel, StickyZoneSTE,
)
CKPT_DIR = os.path.join(os.path.dirname(__file__) or ".", "runs", "ternary-v1")
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
def load_model_from(path):
model = MORPHTernaryModel().to(DEVICE)
if path is None:
return model
ckpt = torch.load(path, map_location=DEVICE, weights_only=False)
model.load_state_dict(ckpt["model_state_dict"])
return model
@torch.no_grad()
def generate(model, seed_bytes, max_new_tokens=300, temperature=0.8, top_k=40):
model.eval()
idx = torch.tensor([seed_bytes], dtype=torch.long, device=DEVICE)
for _ in range(max_new_tokens):
idx_cond = idx[:, -CTX:]
if idx_cond.shape[1] < 3:
break
with torch.autocast("cuda", dtype=torch.bfloat16):
logits, _ = model(idx_cond)
last_logits = logits[:, -1, :] / temperature
if top_k is not None:
v, _ = torch.topk(last_logits, top_k)
last_logits[last_logits < v[:, [-1]]] = float("-inf")
probs = F.softmax(last_logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1)
idx = torch.cat([idx, idx_next], dim=1)
return idx[0].cpu().tolist()
def bytes_to_text(byte_list):
readable = []
for b in byte_list:
if 32 <= b < 127:
readable.append(chr(b))
elif b == 10:
readable.append("\n")
elif b == 13:
readable.append("")
elif b == 9:
readable.append("\t")
elif b >= 256:
readable.append(f"<{b}>")
else:
readable.append(f"\\x{b:02x}")
return "".join(readable)
def byte_repetition_rate(byte_list):
if len(byte_list) < 2:
return 0.0
bigrams = [(byte_list[i], byte_list[i+1]) for i in range(len(byte_list)-1)]
return 1.0 - len(set(bigrams)) / len(bigrams)
def byte_diversity(byte_list):
unique = len(set(b for b in byte_list if b < 256))
return unique / 256.0
def english_word_fraction(byte_list):
text = bytes_to_text(byte_list).lower()
words = text.split()
if not words:
return 0.0
common = {
"the","and","that","have","for","not","with","you","this","but",
"his","they","her","she","will","would","there","their","what","which",
"out","all","were","your","when","who","him","been","has","more",
"my","than","its","can","no","do","is","it","me","so","as","if",
"am","be","of","at","by","an","or","in","to","a","i","on","we",
"our","us","from","them","he","was","are","had","did","shall",
"king","lord","sir","come","good","love","make","thee","thou",
"now","here","then","where","how","why","what","let","go","must",
"enter","exit","exeunt","act","scene",
}
recognized = sum(1 for w in words if w.strip(".,:;!?\"'()") in common)
return recognized / len(words)
def shakespeare_character_ratio(byte_list):
text = bytes_to_text(byte_list)
lines = text.split("\n")
char_lines = 0
total_lines = 0
for line in lines:
stripped = line.strip()
if not stripped:
continue
total_lines += 1
if ":" in stripped and stripped.split(":")[0].strip().isupper():
char_lines += 1
return char_lines / max(total_lines, 1)
def printable_fraction(byte_list):
printable = sum(1 for b in byte_list if (32 <= b < 127) or b in (10, 13, 9))
return printable / max(len(byte_list), 1)
SEEDS = {
"romeo": list(b"ROMEO:\nWhat light through yonder window breaks?\n"),
"king": list(b"KING RICHARD III:\nNow is the winter of our discontent\n"),
"hamlet": list(b"HAMLET:\nTo be, or not to be, that is the question:\n"),
"macbeth": list(b"MACBETH:\nTomorrow, and tomorrow, and tomorrow\n"),
"blank": list(b"\n"),
}
CHECKPOINTS = [
("init", None),
("step5K", os.path.join(CKPT_DIR, "trigram-morph-step5000.pt")),
("best", os.path.join(CKPT_DIR, "trigram-morph-best.pt")),
("step13K", os.path.join(CKPT_DIR, "trigram-morph-step13000.pt")),
("step25K", os.path.join(CKPT_DIR, "trigram-morph-step25000.pt")),
]
TEMPS = [0.5, 0.8, 1.2]
def main():
print(f"Device: {DEVICE}")
print("=" * 90)
n_gen = 400
all_results = {}
for ckpt_label, ckpt_path in CHECKPOINTS:
model = load_model_from(ckpt_path)
print(f"\n{'=' * 90}")
print(f"CHECKPOINT: {ckpt_label}")
print(f"{'=' * 90}")
for seed_name, seed_bytes in SEEDS.items():
for temp in TEMPS:
tag = f"{ckpt_label}/{seed_name}/t{temp}"
tokens = generate(model, seed_bytes, max_new_tokens=n_gen, temperature=temp, top_k=40)
text = bytes_to_text(tokens)
rep = byte_repetition_rate(tokens)
div = byte_diversity(tokens)
eng = english_word_fraction(tokens)
shk = shakespeare_character_ratio(tokens)
prn = printable_fraction(tokens)
all_results[tag] = {
"ckpt": ckpt_label, "seed": seed_name, "temp": temp,
"rep": rep, "div": div, "eng": eng, "shk": shk, "prn": prn,
"text": text,
}
print(f"\n--- {seed_name} seed, temp={temp} ---")
print(f" printable={prn:.2%} diversity={div:.2%} repetition={rep:.2%} english={eng:.2%} shakespeare_fmt={shk:.2%}")
for line in text.split("\n")[:6]:
print(f" | {line}")
remaining_lines = text.split("\n")
if len(remaining_lines) > 6:
print(f" | ... ({len(text)} chars, {len(remaining_lines)} lines)")
del model
if DEVICE == "cuda":
torch.cuda.empty_cache()
print(f"\n\n{'=' * 90}")
print("GENERATION QUALITY TABLE (averaged across seeds)")
print(f"{'=' * 90}")
print(f"{'Checkpoint':<12} {'Temp':>5} {'Print%':>7} {'Divers%':>8} {'Repeat%':>8} {'English%':>9} {'Shakesp%':>9}")
print(f"{'-'*12} {'-'*5} {'-'*7} {'-'*8} {'-'*8} {'-'*9} {'-'*9}")
for ckpt_label, _ in CHECKPOINTS:
for temp in TEMPS:
matching = [r for r in all_results.values() if r["ckpt"] == ckpt_label and r["temp"] == temp]
if not matching:
continue
avg_prn = sum(r["prn"] for r in matching) / len(matching)
avg_div = sum(r["div"] for r in matching) / len(matching)
avg_rep = sum(r["rep"] for r in matching) / len(matching)
avg_eng = sum(r["eng"] for r in matching) / len(matching)
avg_shk = sum(r["shk"] for r in matching) / len(matching)
print(f"{ckpt_label:<12} {temp:>5.1f} {avg_prn:>7.1%} {avg_div:>8.1%} {avg_rep:>8.1%} {avg_eng:>9.1%} {avg_shk:>9.1%}")
if __name__ == "__main__":
main()
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