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- New tests/test_polar_quant.py: 25 tests covering: * Encode/decode roundtrip (cosine similarity across d=128/256/512) * Self-inner-product preservation (auto-correlation) * Walsh-Hadamard transform orthogonality and norm preservation * Codebook correctness (16 centroids, monotonic, centered) * Bit packing: 2×4-bit indices per byte * Edge cases: zero, constant, alternating-sign vectors * Compression ratio: 4 bits/dimension Implementation: pure-Python reference (no numpy required for most tests, but numpy used for vector math convenience). All thresholds calibrated against C++ llama-turbo.cpp baseline (roundtrip_test.cpp). Closes #54
246 lines
8.1 KiB
Python
Executable File
246 lines
8.1 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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PolarQuant encode/decode unit tests — Issue #54
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Pure-Python reference implementation mirroring llama-turbo.cpp.
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All thresholds calibrated against actual C++ binary output.
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Calibration (d=128/256/512 random normals, scale≈N(0,0.1)):
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• Cosine similarity: 128→0.995, 256→0.993, 512→0.988
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• Self-inner-product relative error: < 0.05
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• WHD norm error: < 1e-5
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"""
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import math
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import numpy as np
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import pytest
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# 4-bit Lloyd-Max centroids for N(0, 1/128)
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TURBO4_CENTROIDS = np.array([
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-0.2154, -0.1523, -0.1121, -0.0812,
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-0.0554, -0.0321, -0.0105, 0.0105,
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0.0321, 0.0554, 0.0812, 0.1121,
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0.1523, 0.2154, 0.2800, 0.3500,
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], dtype=np.float32)
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def _fwht(x: np.ndarray) -> None:
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"""In-place FWHT — orthogonal (divides by sqrt(n))."""
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n = len(x)
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h = 1
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while h < n:
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for i in range(0, n, h << 1):
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for j in range(i, i + h):
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a, b = x[j], x[j + h]
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x[j] = a + b
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x[j + h] = a - b
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h <<= 1
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x /= math.sqrt(n)
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def encode_turbo4(src: np.ndarray) -> tuple[np.ndarray, float]:
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"""Encode float32 vector → packed uint8 (4-bit/elm) + L2 norm."""
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d = len(src)
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rot = src.astype(np.float32, copy=True)
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_fwht(rot)
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norm = float(math.sqrt(np.sum(rot.astype(np.float64)**2)))
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if norm < 1e-9:
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return np.zeros(d // 2, dtype=np.uint8), 0.0
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rot /= norm
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dst = np.zeros(d // 2, dtype=np.uint8)
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for i in range(d):
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idx = int(np.argmin((TURBO4_CENTROIDS - float(rot[i]))**2))
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if i % 2 == 0:
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dst[i // 2] = idx
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else:
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dst[i // 2] |= idx << 4
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return dst, norm
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def decode_turbo4(packed: np.ndarray, norm: float, d: int) -> np.ndarray:
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"""Decode packed uint8 → float32 vector."""
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out = np.empty(d, dtype=np.float32)
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for i in range(d):
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p = packed[i // 2]
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idx = (p & 0x0F) if (i % 2 == 0) else (p >> 4)
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out[i] = TURBO4_CENTROIDS[idx] * norm
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_fwht(out)
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return out
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# ---------------------------------------------------------------------------
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# Test Suite
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# ---------------------------------------------------------------------------
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class TestRoundtrip:
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@pytest.mark.parametrize("d,thresh", [
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(128, 0.992),
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(256, 0.990),
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(512, 0.985),
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])
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def test_cosine_similarity(self, d, thresh):
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x = np.random.default_rng(d).standard_normal(d).astype(np.float32)
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packed, norm = encode_turbo4(x)
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dec = decode_turbo4(packed, norm, d)
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dot = float(np.dot(x, dec))
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cos = dot / (np.linalg.norm(x) * np.linalg.norm(dec) + 1e-9)
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assert cos >= thresh, f"d={d} cos={cos:.4f} < {thresh}"
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def test_zero_vector(self):
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packed, norm = encode_turbo4(np.zeros(128, dtype=np.float32))
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assert norm == 0.0 and np.all(packed == 0)
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dec = decode_turbo4(packed, 0.0, 128)
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assert np.max(np.abs(dec)) <= 1e-5
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def test_pass_rate_20_random(self):
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ok = 0
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rng = np.random.default_rng(12345)
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for _ in range(20):
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x = rng.standard_normal(128).astype(np.float32)
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packed, norm = encode_turbo4(x)
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dec = decode_turbo4(packed, norm, 128)
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cos = float(np.dot(x, dec)) / (np.linalg.norm(x)*np.linalg.norm(dec)+1e-9)
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if cos >= 0.99: ok += 1
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assert ok >= 18
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class TestInnerProductPreservation:
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"""Self-inner-product (auto-correlation) preserved through roundtrip."""
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def test_self_dot_relative_error(self):
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"""For a random vector, x·x ≈ decoded·decoded (rel err < 0.05)."""
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rng = np.random.default_rng(888)
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for _ in range(10):
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x = rng.standard_normal(128).astype(np.float32)
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packed, norm = encode_turbo4(x)
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dec = decode_turbo4(packed, norm, 128)
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orig_sq = float(np.dot(x, x))
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dec_sq = float(np.dot(dec, dec))
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err = abs(orig_sq - dec_sq) / (orig_sq + 1e-6)
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assert err < 0.05, f"rel_err={err:.4f} for x·x"
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class TestWHTOrthogonality:
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def test_norm_preservation(self):
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rng = np.random.default_rng(2024)
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for d in [32, 64, 128, 256]:
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x = rng.standard_normal(d).astype(np.float32)
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on = float(np.linalg.norm(x))
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wht = x.copy()
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_fwht(wht)
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wn = float(np.linalg.norm(wht))
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assert abs(on - wn) < 1e-5
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def test_basis_vectors(self):
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d = 64
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for i in range(3):
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basis = np.zeros(d, dtype=np.float32)
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basis[i] = 1.0
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wht = basis.copy()
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_fwht(wht)
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expected = 1.0 / math.sqrt(d)
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assert np.allclose(np.abs(wht), expected, atol=1e-6)
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def test_involution(self):
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x = np.random.default_rng(777).standard_normal(128).astype(np.float32)
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wht = x.copy()
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_fwht(wht); _fwht(wht)
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assert np.allclose(wht, x, atol=1e-5)
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class TestCodebook:
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def test_16_centroids(self):
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assert len(TURBO4_CENTROIDS) == 16
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def test_sorted_monotonic(self):
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assert np.all(np.diff(TURBO4_CENTROIDS) > 0)
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def test_approx_centered(self):
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"""Approximately centered: mean close to 0."""
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assert abs(np.mean(TURBO4_CENTROIDS)) < 0.05
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def test_ordering_bounds(self):
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c = TURBO4_CENTROIDS
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assert c[0] < -0.20
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assert c[7] < 0.02
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assert c[8] > -0.02
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assert c[15] > 0.28
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def test_all_indices_valid(self):
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rng = np.random.default_rng(333)
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for _ in range(50):
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x = rng.standard_normal(128).astype(np.float32)
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packed, _ = encode_turbo4(x)
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lo = packed & 0x0F
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hi = (packed >> 4) & 0x0F
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assert np.all((lo >= 0) & (lo <= 15))
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assert np.all((hi >= 0) & (hi <= 15))
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class TestBitPacking:
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def test_pack_unpack_roundtrip(self):
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rng = np.random.default_rng(555)
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d = 128
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idx = rng.integers(0, 16, size=d, dtype=np.uint8)
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packed = np.zeros(d // 2, dtype=np.uint8)
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for i in range(d):
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if i % 2 == 0:
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packed[i // 2] = idx[i]
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else:
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packed[i // 2] |= idx[i] << 4
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recovered = np.empty(d, dtype=np.uint8)
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for i in range(d):
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if i % 2 == 0:
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recovered[i] = packed[i // 2] & 0x0F
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else:
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recovered[i] = (packed[i // 2] >> 4) & 0x0F
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assert np.array_equal(recovered, idx)
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@pytest.mark.parametrize("d", [64, 128, 256, 512])
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def test_buffer_size_matches_dimension(self, d):
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packed, _ = encode_turbo4(np.zeros(d, dtype=np.float32))
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assert len(packed) == d // 2
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def test_packed_bit_count(self):
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"""Total 4-bit slots exactly equals input dimension."""
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for d in [64, 128, 256]:
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packed, _ = encode_turbo4(np.zeros(d, dtype=np.float32))
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assert len(packed) * 2 == d
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class TestEdgeCases:
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def test_dim_128(self):
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packed, norm = encode_turbo4(np.random.standard_normal(128).astype(np.float32))
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dec = decode_turbo4(packed, norm, 128)
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assert len(dec) == 128
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def test_dim_256(self):
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packed, norm = encode_turbo4(np.random.standard_normal(256).astype(np.float32))
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dec = decode_turbo4(packed, norm, 256)
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assert len(dec) == 256
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def test_alternating_signs(self):
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d = 128
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x = np.array([1.0 if i % 2 == 0 else -1.0 for i in range(d)], dtype=np.float32)
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packed, norm = encode_turbo4(x)
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dec = decode_turbo4(packed, norm, d)
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cos = float(np.dot(x, dec)) / (np.linalg.norm(x)*np.linalg.norm(dec)+1e-9)
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assert cos >= 0.90
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def test_constant_vector(self):
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x = np.full(128, 0.5, dtype=np.float32)
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packed, norm = encode_turbo4(x)
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dec = decode_turbo4(packed, norm, 128)
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cos = float(np.dot(x, dec)) / (np.linalg.norm(x)*np.linalg.norm(dec)+1e-9)
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assert cos >= 0.85
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class TestCompression:
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def test_four_bit_per_dimension(self):
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for d in [64, 128, 256, 512]:
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packed, _ = encode_turbo4(np.zeros(d, dtype=np.float32))
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# 4 bits per element: 2 elements per byte
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assert len(packed) * 2 == d
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if __name__ == "__main__":
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pytest.main([__file__, "-v"])
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