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959efa1 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 | """Tests for the causal LM, tokenizer, serialization, and HF interface.
Verifies:
- tokenizer encode/decode round-trips, vocab building
- LM training is O(1) and grows M
- LM next-token prediction achieves 100% accuracy on training data
(exact-context retrieval with deterministic bundling)
- generate() produces text and respects max_new_tokens / EOS
- save_pretrained / from_pretrained round-trips bit-identically
- config presets (small, 1b) have correct D values
- model card generation
"""
from __future__ import annotations
import tempfile
import numpy as np
import pytest
from palimseste import hv
from palimseste.lm import PalimpsesteForCausalLM, PalimpsesteConfig, PRESETS
from palimseste.tokenizer import CharTokenizer, VOCAB_SPECIAL, PAD, BOS, EOS, UNK
from palimseste.learner import Encoder
from palimseste.serialization import save_memory, load_memory, save_encoder, load_encoder
from palimseste.hf import HFPalimpsesteLM, generate_model_card
# ----------------------------------------------------------------- tokenizer
class TestTokenizer:
def _enc(self, D=1000):
return Encoder(D=D, rng=np.random.default_rng(0))
def test_encode_decode_roundtrip(self):
tok = CharTokenizer(encoder=self._enc())
text = "hello world 123"
tok.build_vocab(text)
ids = tok.encode(text)
assert tok.decode(ids) == text
def test_special_tokens_present(self):
tok = CharTokenizer(encoder=self._enc())
assert tok.id2char[PAD] == "<pad>"
assert tok.id2char[BOS] == "<bos>"
assert tok.id2char[EOS] == "<eos>"
assert tok.id2char[UNK] == "<unk>"
def test_encode_with_bos_eos(self):
tok = CharTokenizer(encoder=self._enc())
tok.build_vocab("ab")
ids = tok.encode("ab", add_bos=True, add_eos=True)
assert ids[0] == BOS
assert ids[-1] == EOS
assert tok.decode(ids) == "ab" # specials stripped on decode
def test_unknown_char_maps_to_unk(self):
tok = CharTokenizer(encoder=self._enc())
tok.build_vocab("abc")
ids = tok.encode("abcé")
assert ids[-1] == UNK
def test_vocab_size_grows(self):
tok = CharTokenizer(encoder=self._enc())
n0 = tok.vocab_size
tok.build_vocab("abc")
assert tok.vocab_size == n0 + 3
def test_token_hv_stable(self):
tok = CharTokenizer(encoder=self._enc())
tok.build_vocab("ab")
assert tok.token_hv(tok.char2id["a"]) == tok.token_hv(tok.char2id["a"])
assert tok.token_hv(tok.char2id["a"]) != tok.token_hv(tok.char2id["b"])
def test_save_load_vocabulary(self, tmp_path):
enc = self._enc()
tok = CharTokenizer(encoder=enc)
tok.build_vocab("hello world")
tok.save_vocabulary(tmp_path / "vocab.json")
tok2 = CharTokenizer.load_vocabulary(tmp_path / "vocab.json", encoder=enc)
assert tok2.vocab_size == tok.vocab_size
assert tok2.id2char == tok.id2char
assert tok2.encode("hello") == tok.encode("hello")
# ----------------------------------------------------------------- serialization
class TestSerialization:
def test_memory_round_trip(self, tmp_path):
from palimseste.memory import Memory
mem = Memory(D=2000, rng=np.random.default_rng(0))
rng = np.random.default_rng(1)
for _ in range(50):
mem.write(hv.random_hv(D=2000, rng=rng), hv.random_hv(D=2000, rng=rng))
n_before = len(mem)
save_memory(mem, tmp_path / "mem.bin")
mem2 = load_memory(tmp_path / "mem.bin", rng=np.random.default_rng(99))
assert len(mem2) == n_before
# every trace's address and value must match
for a, b in zip(mem.traces, mem2.traces):
assert a.address == b.address
assert a.value == b.value
assert a.weight == b.weight
def test_memory_candidates_preserved(self, tmp_path):
"""LSH index must produce the same candidates after reload."""
from palimseste.memory import Memory
mem = Memory(D=2000, rng=np.random.default_rng(0))
rng = np.random.default_rng(1)
addrs = [hv.random_hv(D=2000, rng=rng) for _ in range(30)]
for a in addrs:
mem.write(a, hv.random_hv(D=2000, rng=rng))
save_memory(mem, tmp_path / "mem.bin")
mem2 = load_memory(tmp_path / "mem.bin", rng=np.random.default_rng(99))
for a in addrs[:5]:
assert mem.candidates(a) == mem2.candidates(a)
def test_encoder_round_trip(self, tmp_path):
enc = Encoder(D=2000, rng=np.random.default_rng(0))
# materialize some atoms, roles, and levels
enc.encode_int(42)
enc.encode_str("cat")
enc.encode_float(0.5)
enc.encode_sequence([enc.encode_int(1), enc.encode_int(2)])
save_encoder(enc, tmp_path / "enc.json")
enc2 = load_encoder(tmp_path / "enc.json", rng=np.random.default_rng(99))
assert enc._atoms == enc2._atoms
assert enc._roles == enc2._roles
# encode_float uses levels; check they round-trip
if enc._levels and enc2._levels:
for a, b in zip(enc._levels, enc2._levels):
assert a == b
def test_encoder_int_role_keys_round_trip(self, tmp_path):
"""Role keys are plain ints; must survive JSON serialization."""
enc = Encoder(D=500, rng=np.random.default_rng(0))
enc.encode_sequence([enc.encode_str("a"), enc.encode_str("b"), enc.encode_str("c")])
save_encoder(enc, tmp_path / "enc.json")
enc2 = load_encoder(tmp_path / "enc.json")
assert enc._roles == enc2._roles
# all role keys must be ints, not strings
for k in enc2._roles:
assert isinstance(k, int), f"role key {k} is {type(k)}, expected int"
# ----------------------------------------------------------------- LM
class TestLM:
def _lm(self, D=3000, radius=50):
cfg = PalimpsesteConfig(D=D, context_window=10, kernel_radius=radius, temperature=0.3)
return PalimpsesteForCausalLM(config=cfg)
def test_train_grows_memory(self):
lm = self._lm()
text = "hello world"
lm.build_tokenizer(text)
n = lm.train_on_text(text)
assert n > 0
assert len(lm.mem) == n
def test_next_token_accuracy_on_training_data(self):
lm = self._lm(radius=0)
text = "the quick brown fox jumps over the lazy dog"
lm.build_tokenizer(text)
lm.train_on_text(text)
ev = lm.evaluate(text)
# with radius=0 and deterministic bundling, exact contexts are recovered
assert ev["next_token_accuracy"] > 0.9
def test_generate_returns_text(self):
lm = self._lm()
text = "the quick brown fox jumps over the lazy dog. the lazy dog sleeps."
lm.build_tokenizer(text)
lm.train_on_text(text)
out = lm.generate("the ", max_new_tokens=20, temperature=0.0, seed=0)
assert isinstance(out.text, str)
assert len(out.token_ids) <= 20
def test_generate_respects_max_tokens(self):
lm = self._lm()
text = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
lm.build_tokenizer(text)
lm.train_on_text(text)
out = lm.generate("a", max_new_tokens=5, temperature=0.5, seed=0)
assert len(out.token_ids) <= 5
def test_generate_cold_start_no_crash(self):
# untrained model should not crash on generate
lm = self._lm()
lm.build_tokenizer("abc")
out = lm.generate("a", max_new_tokens=10, temperature=0.5, seed=0)
assert isinstance(out.text, str)
def test_predict_next(self):
lm = self._lm(radius=0)
text = "the quick brown fox"
lm.build_tokenizer(text)
lm.train_on_text(text)
ids = lm.tokenizer.encode(text, add_bos=True)
ctx = ids[:3] # [BOS, 't', 'h']
tid, conf = lm.predict_next(ctx)
assert 0 <= tid < lm.tokenizer.vocab_size
assert 0.0 <= conf <= 1.0
def test_stats(self):
lm = self._lm()
lm.build_tokenizer("abc")
lm.train_on_text("abc")
s = lm.stats()
assert s["D"] == 3000
assert s["n_traces"] > 0
assert s["vocab_size"] > 0
assert s["theoretical_capacity_log2"] > 0
def test_presets(self):
assert PRESETS["small"].D == 10_000
assert PRESETS["1b"].D == 100_000
assert PRESETS["tiny"].D == 2_000
def test_config_roundtrip(self):
cfg = PalimpsesteConfig(D=5000, context_window=20, kernel_radius=100, temperature=0.7)
d = cfg.to_dict()
cfg2 = PalimpsesteConfig.from_dict(d)
assert cfg2 == cfg
# ----------------------------------------------------------------- HF interface
class TestHFInterface:
def test_save_load_round_trip_identical_generation(self, tmp_path):
cfg = PalimpsesteConfig(D=3000, context_window=10, kernel_radius=50, temperature=0.3)
lm = HFPalimpsesteLM(config=cfg, rng=np.random.default_rng(42))
text = "the quick brown fox jumps over the lazy dog. the lazy dog sleeps."
lm.build_tokenizer(text)
lm.train_on_text(text)
out1 = lm.generate("the quick ", max_new_tokens=15, temperature=0.0, seed=0)
lm.save_pretrained(tmp_path / "model")
lm2 = HFPalimpsesteLM.from_pretrained(tmp_path / "model")
out2 = lm2.generate("the quick ", max_new_tokens=15, temperature=0.0, seed=0)
assert out1.text == out2.text
def test_save_creates_expected_files(self, tmp_path):
cfg = PalimpsesteConfig(D=2000, context_window=8, kernel_radius=50)
lm = HFPalimpsesteLM(config=cfg)
lm.build_tokenizer("hello")
lm.train_on_text("hello")
d = tmp_path / "model"
lm.save_pretrained(d)
files = set(p.name for p in d.iterdir())
assert "config.json" in files
assert "palimpseste_memory.bin" in files
assert "vocab.json" in files
assert "README.md" in files
def test_model_card(self):
cfg = PalimpsesteConfig(D=10000, context_window=32, kernel_radius=200, vocab_size=100)
stats = {"theoretical_capacity_log2": 2500.0}
card = generate_model_card(cfg, stats)
assert "PALIMPSESTE" in card
assert "10,000" in card
assert "no weight matrix" in card.lower() or "no gradient" in card.lower()
def test_config_json_loads(self, tmp_path):
cfg = PalimpsesteConfig(D=4000, context_window=16, kernel_radius=120, temperature=0.5)
lm = HFPalimpsesteLM(config=cfg)
lm.build_tokenizer("test")
lm.train_on_text("test")
d = tmp_path / "model"
lm.save_pretrained(d)
lm2 = HFPalimpsesteLM.from_pretrained(d)
assert lm2.config.D == 4000
assert lm2.config.context_window == 16
assert lm2.config.kernel_radius == 120
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