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20857b0 | 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 | """Dense bit-packing for neural tokens.
MAGVIT2's vocabulary is 262,144 (2^18), so each token fits in 18 bits.
Storing them as 32-bit integers wastes 44% of bits before entropy coding.
Pack uses numpy vectorized operations for ~7x speedup over Python.
"""
import numpy as np
def pack_tokens(tokens: list[int], bits: int = 18) -> list[int]:
if not tokens:
return []
n = len(tokens)
total_bits = n * bits
out_len = (total_bits + 7) // 8
arr = np.array(tokens, dtype=np.uint64)
out = np.zeros(out_len, dtype=np.uint8)
for bit_idx in range(bits):
bit_val = (arr >> (bits - 1 - bit_idx)) & 1
positions = np.arange(n, dtype=np.uint64) * bits + bit_idx
byte_idx = positions >> 3
bit_in_byte = 7 - (positions & 7)
out[byte_idx] |= bit_val.astype(np.uint8) << bit_in_byte.astype(np.uint8)
return out.tolist()
def unpack_tokens(data: list[int], count: int, bits: int = 18) -> list[int]:
if count == 0:
return []
arr = np.array(data, dtype=np.uint8)
nbits = count * bits
raw_bits = np.unpackbits(arr, bitorder='big')
if len(raw_bits) > nbits:
raw_bits = raw_bits[:nbits]
elif len(raw_bits) < nbits:
raw_bits = np.pad(raw_bits, (0, nbits - len(raw_bits)), constant_values=0)
bits_2d = raw_bits.reshape(count, bits)
weights = 1 << np.arange(bits - 1, -1, -1, dtype=np.uint64)
return (bits_2d.astype(np.uint64) @ weights).tolist()
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