quantize-py

python bindings for quantize, a simple, fast quantization library written in rust. quantization stores numbers in fewer bits, trading a little accuracy for a lot less memory.

pip install quantize-py

requires python 3.12 or newer. numpy is installed with it.

from quantize import Scale, quantize

weights = [0.42, -0.10, 0.70, -0.50]

q = quantize(weights, bits=8, block=32, scale=Scale.F16)
back = q.dequantize()  # [0.421, -0.098, 0.700, -0.498]
dot = q.dot(weights)  # 0.926

bits is the width of each code, from 2 to 16. block is how many values share one scale, and scale is how that scale is stored: Scale.F32 (the default), Scale.F16, or Scale.BF16. values can be a list, a numpy array, or anything else np.asarray reads, like a pytorch tensor. a 2-d array keeps its shape, so q.dequantize() gives back a matrix and q.matmul(x) computes x @ W.T, like a linear layer. q.shape gives its rows and columns, and len(q) the number of values, as in rust.

the scales count toward the size: 4-bit codes with one f16 scale per 32 values cost 4.5 bits per value, or 5 with the default f32 scale. q.bits_per_element reports it. above about 10 bits, use f32 scales with asymmetric.quantize, since f16 and bf16 zero-points cap its accuracy.

the other schemes return the same Quantized type:

• asymmetric.quantize(weights, bits=8, block=32) adds a zero-point per block, for values that aren't centered on zero
• adaptive.quantize(weights, tolerance=0.1 * weights.std()) gives each block the fewest bits, from 2 to 8, that round every weight within tolerance, in the weights' own units. a tenth of their standard deviation gives about 5 bits a block. for a list, use np.std(weights)
• learned.refine(q, weights) refits each block's scale, and its zero-point if it has one, to lower the mean squared error. it changes q in place, so call q.copy() first to keep the original
• learned.alternate(q, weights) refits too, then rounds each value to the nearest code on its block's new line, and repeats until no code moves. it also changes q in place. both can raise the worst error past an adaptive tensor's tolerance
• Scheme.Q4_32.quantize(weights) picks a scheme at run time

a block with outliers can need more than 8 bits, which raises ToleranceTooTightError. retrying with its smallest_tolerance works, but loosens every block, not just that one:

try:
    q = adaptive.quantize(weights, tolerance=0.1 * weights.std())
except ToleranceTooTightError as error:
    q = adaptive.quantize(weights, tolerance=error.smallest_tolerance)

quantized values can be pickled, and compared with ==. q.to_bytes() saves one as bytes, in the same format as the rust crate, and Quantized.from_bytes(data) loads it back. to keep it in an np.savez or safetensors file, store np.frombuffer(q.to_bytes(), np.uint8).

to save its parts as plain arrays instead, like with np.savez, pass them back by name to Quantized.from_parts. leave out the one that's None: bits for an adaptive tensor, or block_bits for the others:

parts = dict(kind=q.kind, shape=q.shape, block=q.block, bits=q.bits, block_bits=q.block_bits,
             codes=q.codes, scales=q.scales, zero_points=q.zero_points, scale=q.scale.name)
np.savez("layer.npz", **{name: part for name, part in parts.items() if part is not None})
q = Quantized.from_parts(**np.load("layer.npz"))

to load quantized values from a torch.save checkpoint, call torch.serialization.add_safe_globals([Quantized]) before torch.load.

each value decodes as code * scale, or (code - zero_point) * scale with zero-points, using the scale and zero-point of its block. codes are signed and bits wide, and q.codes packs them low bits first. scales can be negative, since a symmetric block puts its value farthest from zero on the most negative code. help(Quantized) has the details.

to build and test from a clone of the repo, with rust 1.88 or newer and just:

just setup
just python

on debian or ubuntu, run sudo apt install python3-venv first.