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117 lines
3.5 KiB
Python
117 lines
3.5 KiB
Python
"""Quantization utilities for a numpy array/tensor."""
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from copy import deepcopy
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from typing import Optional
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import numpy
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STABILITY_CONST = 10 ** -6
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class QuantizedArray:
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"""Abstraction of quantized array."""
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def __init__(self, n_bits: int, values: numpy.ndarray, is_signed=False):
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"""Quantize an array.
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See https://arxiv.org/abs/1712.05877.
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Args:
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values (numpy.ndarray): Values to be quantized.
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n_bits (int): The number of bits to use for quantization.
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is_signed (bool): Whether the quantization can be on signed integers.
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"""
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self.offset = 0
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if is_signed:
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self.offset = 2 ** (n_bits - 1)
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self.values = values
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self.n_bits = n_bits
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self.is_signed = is_signed
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self.scale, self.zero_point, self.qvalues = self.compute_quantization_parameters()
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self.n_features = 1 if len(values.shape) <= 1 else values.shape[1]
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def __call__(self) -> Optional[numpy.ndarray]:
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return self.qvalues
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def compute_quantization_parameters(self):
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"""Compute the quantization parameters."""
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# Small constant needed for stability
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rmax = numpy.max(self.values)
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rmin = numpy.min(self.values)
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if rmax - rmin < STABILITY_CONST:
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scale = 1
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zero_point = rmin
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else:
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scale = (
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(rmax - rmin) / ((2 ** self.n_bits - 1 - self.offset) - (-self.offset))
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if rmax != rmin
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else 1.0
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)
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zero_point = numpy.round(
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(rmax * (-self.offset) - (rmin * (2 ** self.n_bits - 1 - self.offset)))
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/ (rmax - rmin)
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).astype(int)
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# Compute quantized values and store
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qvalues = self.values / scale + zero_point
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qvalues = (
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qvalues.round()
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.clip(-self.offset, 2 ** (self.n_bits) - 1 - self.offset)
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.astype(int) # Careful this can be very large with high number of bits
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)
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return scale, zero_point, qvalues
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def update_values(self, values: numpy.ndarray) -> Optional[numpy.ndarray]:
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"""Update values to get their corresponding qvalues using the related quantized parameters.
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Args:
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values (numpy.ndarray): Values to replace self.values
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Returns:
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qvalues (numpy.ndarray): Corresponding qvalues
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"""
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self.values = deepcopy(values)
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self.quant()
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return self.qvalues
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def update_qvalues(self, qvalues: numpy.ndarray) -> Optional[numpy.ndarray]:
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"""Update qvalues to get their corresponding values using the related quantized parameters.
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Args:
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qvalues (numpy.ndarray): Values to replace self.qvalues
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Returns:
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values (numpy.ndarray): Corresponding values
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"""
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self.qvalues = deepcopy(qvalues)
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self.dequant()
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return self.values
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def quant(self) -> Optional[numpy.ndarray]:
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"""Quantize self.values.
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Returns:
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numpy.ndarray: Quantized values.
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"""
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self.qvalues = (
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(self.values / self.scale + self.zero_point)
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.round()
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.clip(-self.offset, 2 ** (self.n_bits) - 1 - self.offset)
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.astype(int)
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)
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return self.qvalues
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def dequant(self) -> numpy.ndarray:
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"""Dequantize self.qvalues.
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Returns:
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numpy.ndarray: Dequantized values.
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"""
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self.values = self.scale * (self.qvalues - self.zero_point)
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return self.values
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