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49 lines
1.7 KiB
Python

import paddle
import numpy as np
import os
import paddle.nn as nn
import paddleslim
class PACT(paddle.nn.Layer):
def __init__(self):
super(PACT, self).__init__()
alpha_attr = paddle.ParamAttr(
name=self.full_name() + ".pact",
initializer=paddle.nn.initializer.Constant(value=20),
learning_rate=1.0,
regularizer=paddle.regularizer.L2Decay(2e-5),
)
self.alpha = self.create_parameter(shape=[1], attr=alpha_attr, dtype="float32")
def forward(self, x):
out_left = paddle.nn.functional.relu(x - self.alpha)
out_right = paddle.nn.functional.relu(-self.alpha - x)
x = x - out_left + out_right
return x
quant_config = {
# weight preprocess type, default is None and no preprocessing is performed.
"weight_preprocess_type": None,
# activation preprocess type, default is None and no preprocessing is performed.
"activation_preprocess_type": None,
# weight quantize type, default is 'channel_wise_abs_max'
"weight_quantize_type": "channel_wise_abs_max",
# activation quantize type, default is 'moving_average_abs_max'
"activation_quantize_type": "moving_average_abs_max",
# weight quantize bit num, default is 8
"weight_bits": 8,
# activation quantize bit num, default is 8
"activation_bits": 8,
# data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
"dtype": "int8",
# window size for 'range_abs_max' quantization. default is 10000
"window_size": 10000,
# The decay coefficient of moving average, default is 0.9
"moving_rate": 0.9,
# for dygraph quantization, layers of type in quantizable_layer_type will be quantized
"quantizable_layer_type": ["Conv2D", "Linear"],
}