File size: 10,370 Bytes
763f100 | 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 BPE tokenizer, HV attention, and abstraction engine."""
from __future__ import annotations
import pytest
import numpy as np
from palimseste import hv
from palimseste.learner import Encoder
from palimseste.bpe import BPETokenizer
from palimseste.attention import HVAttention, AttentionConfig
from palimseste.abstraction import AbstractionEngine, AbstractionConfig
from palimseste.memory import Memory
from palimseste.lm import PalimpsesteForCausalLM, PalimpsesteConfig
# ================================================================ BPE
class TestBPE:
def _tok(self, D=2000):
enc = Encoder(D=D, rng=np.random.default_rng(0))
return BPETokenizer(encoder=enc, vocab_size=1000)
def test_train_and_encode(self):
tok = self._tok()
text = "hello world hello world hello world hello world"
tok.train(text, target_vocab_size=300)
ids = tok.encode("hello world")
assert len(ids) > 0
assert len(ids) < len("hello world") # fewer tokens than chars
def test_encode_decode_roundtrip(self):
tok = self._tok()
text = "the quick brown fox jumps over the lazy dog"
tok.train(text * 10, target_vocab_size=500)
ids = tok.encode(text)
decoded = tok.decode(ids)
assert decoded == text
def test_bpe_reduces_sequence_length(self):
"""BPE should produce fewer tokens than char-level."""
tok = self._tok()
text = "hello world " * 50
tok.train(text, target_vocab_size=2000)
bpe_ids = tok.encode("hello world hello world")
char_count = len("hello world hello world")
assert len(bpe_ids) < char_count, f"BPE {len(bpe_ids)} should be < chars {char_count}"
def test_bos_eos(self):
tok = self._tok()
tok.train("abc abc abc", target_vocab_size=300)
ids = tok.encode("abc", add_bos=True, add_eos=True)
assert ids[0] == 1 # BOS
assert ids[-1] == 2 # EOS
def test_token_hv_stable(self):
tok = self._tok()
tok.train("hello hello hello", target_vocab_size=300)
h1 = tok.token_hv(4)
h2 = tok.token_hv(4)
assert h1 == h2
def test_save_load(self, tmp_path):
tok = self._tok()
tok.train("test test test " * 20, target_vocab_size=300)
ids_before = tok.encode("test test")
tok.save_vocabulary(tmp_path / "bpe.json")
enc = Encoder(D=2000, rng=np.random.default_rng(0))
tok2 = BPETokenizer.load_vocabulary(tmp_path / "bpe.json", encoder=enc)
ids_after = tok2.encode("test test")
assert ids_before == ids_after
def test_encode_context(self):
tok = self._tok(D=2000)
tok.train("hello world test " * 20, target_vocab_size=500)
ids = tok.encode("hello world test")
ctx_hv = tok.encode_context(ids, window=32)
assert isinstance(ctx_hv, hv.HV)
assert ctx_hv.D == 2000
# ================================================================ Attention
class TestHVAttention:
def _enc(self, D=2000):
return Encoder(D=D, rng=np.random.default_rng(0))
def test_attend_returns_hv(self):
enc = self._enc()
att = HVAttention(config=AttentionConfig(max_context=64, top_k=8),
encoder=enc)
rng = np.random.default_rng(1)
tokens = [hv.random_hv(2000, rng=rng) for _ in range(20)]
query = hv.random_hv(2000, rng=rng)
result = att.attend(tokens, query)
assert isinstance(result, hv.HV)
assert result.D == 2000
def test_attention_is_selective(self):
"""The attended HV should be more similar to the query-matching
tokens than to a random HV."""
enc = self._enc(D=5000)
att = HVAttention(config=AttentionConfig(max_context=64, top_k=4, temperature=2.0),
encoder=enc)
rng = np.random.default_rng(2)
# create tokens where one is very similar to the query
query = hv.random_hv(5000, rng=rng)
near_token = query # identical = maximum attention
far_tokens = [hv.random_hv(5000, rng=rng) for _ in range(20)]
tokens = far_tokens[:10] + [near_token] + far_tokens[10:]
result = att.attend(tokens, query)
# result should be more similar to query than a random HV
random_hv = hv.random_hv(5000, rng=rng)
assert hv.similarity(result, query) > hv.similarity(random_hv, query)
def test_top_k_limits_selection(self):
enc = self._enc()
att = HVAttention(config=AttentionConfig(max_context=32, top_k=4),
encoder=enc)
rng = np.random.default_rng(3)
tokens = [hv.random_hv(2000, rng=rng) for _ in range(32)]
query = hv.random_hv(2000, rng=rng)
result = att.attend(tokens, query)
assert isinstance(result, hv.HV)
def test_empty_context(self):
enc = self._enc()
att = HVAttention(config=AttentionConfig(), encoder=enc)
query = hv.random_hv(2000, rng=np.random.default_rng(0))
result = att.attend([], query)
assert result == query
def test_max_context_truncation(self):
enc = self._enc()
att = HVAttention(config=AttentionConfig(max_context=10, top_k=5),
encoder=enc)
rng = np.random.default_rng(4)
tokens = [hv.random_hv(2000, rng=rng) for _ in range(50)]
query = hv.random_hv(2000, rng=rng)
# should not crash with more tokens than max_context
result = att.attend(tokens, query)
assert isinstance(result, hv.HV)
def test_config_validation(self):
with pytest.raises(ValueError):
AttentionConfig(max_context=0)
with pytest.raises(ValueError):
AttentionConfig(top_k=0)
with pytest.raises(ValueError):
AttentionConfig(top_k=100, max_context=50)
# ================================================================ Abstraction
class TestAbstraction:
def _mem(self, D=2000):
return Memory(D=D, rng=np.random.default_rng(0))
def test_extract_concepts_from_similar_traces(self):
"""Traces with similar addresses should form a cluster."""
mem = self._mem(D=3000)
rng = np.random.default_rng(1)
base = hv.random_hv(3000, rng=rng)
# create traces with addresses near `base`
for i in range(10):
signs = hv.bits_to_signs(base)
# flip a few bits to make it similar but not identical
flip = rng.choice(3000, size=20, replace=False)
signs[flip] = -signs[flip]
addr = hv.signs_to_bits(signs)
val = hv.random_hv(3000, rng=rng)
mem.write(addr, val, tag=f"item_{i}")
engine = AbstractionEngine(
mem=mem,
config=AbstractionConfig(
n_clusters=10,
min_cluster_size=3,
similarity_threshold=0.2,
sample_size=100,
),
rng=rng,
)
concepts = engine.extract_concepts()
assert len(concepts) > 0
assert concepts[0].n_members >= 3
def test_no_concepts_from_dissimilar_traces(self):
mem = self._mem(D=3000)
rng = np.random.default_rng(2)
for _ in range(20):
mem.write(hv.random_hv(3000, rng=rng), hv.random_hv(3000, rng=rng))
engine = AbstractionEngine(
mem=mem,
config=AbstractionConfig(
similarity_threshold=0.9, # very strict
min_cluster_size=3,
sample_size=100,
),
rng=rng,
)
concepts = engine.extract_concepts()
assert len(concepts) == 0
def test_find_concept(self):
mem = self._mem(D=3000)
rng = np.random.default_rng(3)
base = hv.random_hv(3000, rng=rng)
for i in range(5):
signs = hv.bits_to_signs(base)
flip = rng.choice(3000, size=10, replace=False)
signs[flip] = -signs[flip]
mem.write(hv.signs_to_bits(signs), hv.random_hv(3000, rng=rng))
engine = AbstractionEngine(
mem=mem,
config=AbstractionConfig(similarity_threshold=0.3, min_cluster_size=2),
rng=rng,
)
engine.extract_concepts()
# query with something near the base
concept = engine.find_concept(base)
assert concept is not None
def test_find_concept_returns_none_for_dissimilar(self):
mem = self._mem(D=2000)
rng = np.random.default_rng(4)
for _ in range(5):
mem.write(hv.random_hv(2000, rng=rng), hv.random_hv(2000, rng=rng))
engine = AbstractionEngine(mem=mem, rng=rng)
engine.extract_concepts()
result = engine.find_concept(hv.random_hv(2000, rng=rng))
# might be None or a weak match
assert result is None or isinstance(result, object)
def test_concepts_stored_in_memory(self):
"""Extracted concept centroids should be written into M."""
mem = self._mem(D=3000)
rng = np.random.default_rng(5)
base = hv.random_hv(3000, rng=rng)
for i in range(10):
signs = hv.bits_to_signs(base)
flip = rng.choice(3000, size=15, replace=False)
signs[flip] = -signs[flip]
mem.write(hv.signs_to_bits(signs), hv.random_hv(3000, rng=rng))
n_before = len(mem)
engine = AbstractionEngine(
mem=mem,
config=AbstractionConfig(similarity_threshold=0.2, min_cluster_size=3),
rng=rng,
)
engine.extract_concepts()
assert len(mem) > n_before # concept centroids were added
def test_empty_memory(self):
mem = self._mem(D=2000)
engine = AbstractionEngine(mem=mem, rng=np.random.default_rng(0))
concepts = engine.extract_concepts()
assert concepts == []
def test_config_validation(self):
with pytest.raises(ValueError):
AbstractionConfig(n_clusters=0)
with pytest.raises(ValueError):
AbstractionConfig(min_cluster_size=1)
with pytest.raises(ValueError):
AbstractionConfig(similarity_threshold=0)
with pytest.raises(ValueError):
AbstractionConfig(similarity_threshold=1.5)
|