# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import numpy as np import math from op_test import OpTest def quantize_max_abs(x, max_range): scale = np.max(np.abs(x).flatten()) y = np.round(x / scale * max_range) return y, scale def dequantize_max_abs(x, scale, max_range): y = (scale / max_range) * x return y def channel_wise_quantize_max_abs(x, quant_bit=8): scales = [] for i in range(x.shape[0]): scales.append(np.max(np.abs(x[i])).astype("float32")) y = x.copy() max_range = math.pow(2, quant_bit - 1) - 1 for i, scale in enumerate(scales): y[i] = np.round(y[i] / scale * max_range) return y, scales def channel_wise_dequantize_max_abs(x, scales, quant_bits, activation_scale=None): y = x.copy() for i in range(x.shape[0]): y[i] = (scales[i] / (math.pow(2, quant_bits[0] - 1) - 1)) * y[i] if activation_scale is not None: y *= activation_scale / (math.pow(2, quant_bits[1] - 1) - 1) return y class TestFakeChannelWiseDequantizeMaxAbsOpTwoScales(OpTest): def set_args(self): self.quant_bits = [8, 8] self.data_type = "float32" self.activation_scale = 0.7861 def setUp(self): self.set_args() self.op_type = "fake_channel_wise_dequantize_max_abs" x = np.random.randn(4, 3, 64, 64).astype(self.data_type) yq, scales = channel_wise_quantize_max_abs(x, self.quant_bits[0]) ydq = channel_wise_dequantize_max_abs(yq, scales, self.quant_bits, self.activation_scale) self.inputs = { 'X': yq, 'Scales': [("scales0", np.array(scales).astype(self.data_type)), ("scales1", np.array( [self.activation_scale]).astype(self.data_type))] } self.attrs = {'quant_bits': self.quant_bits} self.outputs = {'Out': ydq} def test_check_output(self): self.check_output() class TestFakeChannelWiseDequantizeMaxAbsOpOneScale(OpTest): def set_args(self): self.quant_bits = [8] self.data_type = "float32" def setUp(self): self.set_args() self.op_type = "fake_channel_wise_dequantize_max_abs" x = np.random.randn(4, 3, 64, 64).astype(self.data_type) yq, scales = channel_wise_quantize_max_abs(x, self.quant_bits[0]) ydq = channel_wise_dequantize_max_abs(yq, scales, self.quant_bits) self.inputs = { 'X': yq, 'Scales': [("scales0", np.array(scales).astype(self.data_type))] } self.attrs = {'quant_bits': self.quant_bits} self.outputs = {'Out': ydq} def test_check_output(self): self.check_output() class TestFakeDequantizeMaxAbsOp(OpTest): def set_args(self): self.num_bits = 8 self.max_range = math.pow(2, self.num_bits - 1) - 1 self.data_type = "float32" def setUp(self): self.set_args() self.op_type = "fake_dequantize_max_abs" x = np.random.randn(31, 65).astype(self.data_type) yq, scale = quantize_max_abs(x, self.max_range) ydq = dequantize_max_abs(yq, scale, self.max_range) self.inputs = {'X': yq, 'Scale': np.array(scale).astype(self.data_type)} self.attrs = {'max_range': self.max_range} self.outputs = {'Out': ydq} def test_check_output(self): self.check_output() class TestFakeDequantizeMaxAbsOpDouble(TestFakeDequantizeMaxAbsOp): def set_args(self): self.num_bits = 8 self.max_range = math.pow(2, self.num_bits - 1) - 1 self.data_type = "float64" class TestFakeDequantizeMaxAbsOp5Bits(TestFakeDequantizeMaxAbsOp): def set_args(self): self.num_bits = 5 self.max_range = math.pow(2, self.num_bits - 1) - 1 self.data_type = "float32" if __name__ == "__main__": unittest.main()