Created
March 28, 2023 12:15
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Post-Train Quantization for llama.cpp in Python
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import numpy as np | |
def pack(xs, dtype='q4_0'): | |
assert dtype == 'q4_0', 'Only quantized int4 type is supported.' | |
assert xs.size % 2 == 0, 'Only arrays of even length.' | |
# Estimate magnitude of array elements and its inverse. | |
amax = abs(xs).max() | |
magnitude = amax / 0b0111 | |
precision = np.float32(1) / magnitude if magnitude else np.float32(0) | |
# Quantize elements. | |
xs = xs.flatten() | |
xs = (xs * precision).astype(np.int8) + 8 | |
ys = xs[::2] | (xs[1::2] << 4) | |
# Append magnitude to the end of int8 array for unpacking. | |
footer = amax.tobytes() + magnitude.astype(np.float32).tobytes() | |
zs = np.frombuffer(footer, np.int8) | |
return np.hstack([ys, zs]) | |
def unpack(xs: np.ndarray): | |
assert xs.ndim == 1, 'Only int8 sequences are supported.' | |
assert xs.size >= 8, 'Too short array.' | |
# Restore magnitude of quantization. | |
amax, magnitude = np.frombuffer(xs[-8:].tobytes(), np.float32) | |
# Restore sequence elements to array with stride 2 (interleaving). | |
xs = xs[:-8] | |
zs = np.zeros((xs.size, 2), np.float32) | |
zs[:, 0] = magnitude * ((xs & 0x0f) - 8) | |
zs[:, 1] = magnitude * (((xs & 0xf0) >> 4) - 8) | |
# Flatten array in order to restore sequence of elements. | |
return zs.flatten() | |
def test_pack_unpack(): | |
xs = np.random.randn(100) | |
xs = np.arange(100) | |
xs -= xs.size // 2 | |
print('original') | |
print(xs) | |
print('packed') | |
ys = pack(xs.astype(np.float32)) | |
print(ys) | |
print('unpacked') | |
zs = unpack(ys) | |
print(zs) | |
print('absolute errors') | |
aerr = zs - xs | |
print(aerr) | |
print('relateive error') | |
rerr = np.linalg.norm(aerr) / np.linalg.norm(xs) | |
print(rerr) |
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