test_fake_quantize_op.py

# Copyright (c) 2022 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 itertools
import numpy as np
import math
from op_test import OpTest


def round_c_single_element(val):
    dtype = type(val)
    if val >= 0:
        return dtype(np.floor(val + 0.5))
    return dtype(np.ceil(val - 0.5))


# rounding to nearest ties away from zero
round_c = np.vectorize(round_c_single_element)


def get_compute_type(dtype):
    assert dtype in [np.float16, np.float32, np.float64]
    if dtype == np.float16:
        return np.float32
    return dtype


class TestFakeQuantizeAbsMaxOp(OpTest):

    def setUp(self):
        self.op_type = 'fake_quantize_abs_max'
        self.attrs = {'bit_length': 8}

    def _fake_quantize_abs_max(self,
                               dtype,
                               input_shape,
                               distribution,
                               round_type='TiesToEven'):
        input_data = distribution(input_shape).astype(dtype)
        compute_type = get_compute_type(dtype)
        scale = np.max(np.abs(input_data))
        bnt = (1 << (self.attrs['bit_length'] - 1)) - 1
        inv_scale = 1.0 / (scale + 1e-6) if scale < 1e-30 else 1.0 / scale
        if round_type == 'TiesToEven':
            round_out = np.round(
                input_data.astype(compute_type) * inv_scale * bnt)
            self.attrs['round_type'] = 0
        else:
            round_out = round_c(
                input_data.astype(compute_type) * inv_scale * bnt)
            self.attrs['round_type'] = 1
        output_data = np.clip(round_out, -bnt - 1, bnt)
        self.inputs = {'X': input_data}
        self.outputs = {'Out': output_data, 'OutScale': scale}
        self.dtype = dtype
        self.check_output()

    def test_fake_quantize_abs_max(self):
        self._fake_quantize_abs_max(np.float32, (124, 240), np.random.random)

    def test_fake_quantize_abs_max_round1(self):
        self._fake_quantize_abs_max(np.float32, (124, 240),
                                    np.random.random,
                                    round_type='TiesAwayFromZero')

    def test_fake_quantize_abs_max_float16(self):
        self._fake_quantize_abs_max(np.float16, (124, 240), np.random.random)

    def test_fake_quantize_abs_max_underflow(self):
        self._fake_quantize_abs_max(np.float32, (10, 10), np.zeros)

    def test_fake_quantize_abs_max_underflow2(self):
        self._fake_quantize_abs_max(np.float32, (10, 10),
                                    lambda shape: np.full(shape, 1e-40))


class TestFakeChannelWiseQuantizeAbsMaxOp(OpTest):

    def setUp(self):
        self.op_type = 'fake_channel_wise_quantize_abs_max'
        self.attrs = {'bit_length': 8}

    def _fake_channel_wise_quantize_abs_max(self,
                                            dtype,
                                            input_shape,
                                            quant_axis,
                                            distribution,
                                            round_type='TiesToEven'):
        assert quant_axis in [0, 1], 'quant_axis should be 0 or 1.'
        input_data = distribution(input_shape).astype(dtype)
        compute_type = get_compute_type(dtype)
        bnt = (1 << (self.attrs['bit_length'] - 1)) - 1
        compute_axis = tuple(i for i in range(len(input_shape))
                             if i != quant_axis)
        scale_broadcast = np.amax(input_data, axis=compute_axis, keepdims=True)
        if round_type == 'TiesToEven':
            round_out = np.round(
                input_data.astype(compute_type) / scale_broadcast * bnt)
            self.attrs['round_type'] = 0
        else:
            round_out = round_c(
                input_data.astype(compute_type) / scale_broadcast * bnt)
            self.attrs['round_type'] = 1
        output_data = np.clip(round_out, -bnt - 1, bnt)
        if quant_axis == 1:
            scale_broadcast = np.transpose(scale_broadcast,
                                           (1, ) + compute_axis)
        scale = scale_broadcast.reshape(input_shape[quant_axis], -1)[:, 0]
        self.inputs = {'X': input_data}
        self.outputs = {'Out': output_data, 'OutScale': scale}
        self.dtype = dtype
        self.attrs['quant_axis'] = quant_axis
        self.check_output()

    def test_fake_channel_wise_quantize_abs_max(self):
        dtype_options = [np.float32, np.float16]
        input_shape_quant_axis_options = [[(20, 15, 6, 6), 0],
                                          [(20, 15, 6, 6), 1], [(30, 30), 0],
                                          [(30, 30), 1]]
        round_type_options = ['TiesToEven', 'TiesAwayFromZero']
        for dtype, input_shape_quant_axis, round_type in itertools.product(
                dtype_options, input_shape_quant_axis_options,
                round_type_options):
            input_shape, quant_axis = input_shape_quant_axis
            with self.subTest(dtype=dtype,
                              input_shape=input_shape,
                              quant_axis=quant_axis,
                              round_type=round_type):
                self._fake_channel_wise_quantize_abs_max(
                    dtype, input_shape, quant_axis, np.random.random,
                    round_type)


class TestFakeQuantizeRangeAbsMaxOp(OpTest):

    def setUp(self):
        self.op_type = 'fake_quantize_range_abs_max'
        self.attrs = {'bit_length': 5, 'window_size': 1}

    def _fake_quantize_range_abs_max(self,
                                     dtype,
                                     input_shape,
                                     distribution,
                                     is_test=False,
                                     round_type='TiesToEven'):
        input_data = distribution(input_shape).astype(dtype)
        compute_type = get_compute_type(dtype)
        bnt = (1 << (self.attrs['bit_length'] - 1)) - 1
        in_scale = np.zeros(1).astype(dtype)
        out_scale = np.zeros(self.attrs['window_size']).astype(dtype)
        out_scale[0] = np.max(np.abs(input_data))
        if is_test:
            out_scale[0] = in_scale[0] = out_scale[0] - 1.0
        if round_type == 'TiesToEven':
            round_out = np.round(
                input_data.astype(compute_type) / out_scale[0] * bnt)
            self.attrs['round_type'] = 0
        else:
            round_out = round_c(
                input_data.astype(compute_type) / out_scale[0] * bnt)
            self.attrs['round_type'] = 1
        output_data = np.clip(round_out, -bnt - 1, bnt)
        self.inputs = {
            'X': input_data,
            'Iter': np.zeros(1).astype(np.int64),
            'InScale': in_scale
        }
        self.outputs = {
            'Out': output_data,
            'OutScale': out_scale[0],
            'OutScales': out_scale
        }
        self.dtype = dtype
        self.attrs['is_test'] = is_test
        self.check_output()

    def test_fake_quantize_range_abs_max(self):
        dtype_options = [np.float16, np.float32]
        is_test_options = [False, True]
        round_type_options = ['TiesToEven', 'TiesAwayFromZero']
        for dtype, is_test, round_type in itertools.product(
                dtype_options, is_test_options, round_type_options):
            self.attrs['bit_length'] = 8 if is_test else 5
            with self.subTest(dtype=dtype,
                              is_test=is_test,
                              round_type=round_type):
                self._fake_quantize_range_abs_max(
                    dtype, (8, 16, 6, 6),
                    lambda shape: (np.random.random(shape) - 0.4) * 10,
                    is_test=is_test,
                    round_type=round_type)


class TestMovingAverageAbsMaxScaleOp(OpTest):

    def setUp(self):
        self.op_type = 'moving_average_abs_max_scale'
        self.attrs = {'moving_rate': float(0.9), 'is_test': False}

    def _moving_average_abs_max_scale(self, dtype, input_shape, distribution):
        input_data = distribution(input_shape).astype(dtype)
        in_accum = np.ones(1).astype(dtype)
        in_state = np.ones(1).astype(dtype)
        out_accum = self.attrs['moving_rate'] * in_accum[0] + np.max(
            np.abs(input_data))
        out_state = self.attrs['moving_rate'] * in_state[0] + 1.0
        out_scale = out_accum / out_state
        self.inputs = {
            'X': input_data,
            'InAccum': in_accum,
            'InState': in_state
        }
        self.outputs = {
            'Out': input_data,
            'OutAccum': out_accum,
            'OutState': out_state,
            'OutScale': out_scale
        }
        self.dtype = dtype
        self.check_output()

    def test_moving_average_abs_max(self):
        self._moving_average_abs_max_scale(np.float32, (8, 16, 7, 7),
                                           np.random.random)


class TestFakeQuantizeMovingAverageAbsMaxOp(OpTest):

    def setUp(self):
        self.op_type = 'fake_quantize_moving_average_abs_max'
        self.attrs = {'bit_length': 5, 'moving_rate': 0.9, 'is_test': False}

    def _fake_quantize_moving_average_abs_max(self,
                                              dtype,
                                              input_shape,
                                              distribution,
                                              dequantize=False,
                                              with_gradient=False,
                                              round_type='TiesToEven'):
        input_data = distribution(input_shape).astype(dtype)
        compute_type = get_compute_type(dtype)
        bnt = (1 << (self.attrs['bit_length'] - 1)) - 1
        in_accum = np.ones(1).astype(dtype)
        in_state = np.ones(1).astype(dtype)
        in_scale = np.array([0.001]).astype(dtype)
        out_accum = np.zeros(1).astype(dtype)
        out_state = np.zeros(1).astype(dtype)
        out_scale = np.zeros(1).astype(dtype)
        out_accum[0] = self.attrs['moving_rate'] * in_accum[0] + np.max(
            np.abs(input_data))
        out_state[0] = self.attrs['moving_rate'] * in_state[0] + 1.0
        out_scale = out_accum / out_state
        if round_type == 'TiesToEven':
            round_out = np.round(
                input_data.astype(compute_type) / out_scale * bnt)
            self.attrs['round_type'] = 0
        else:
            round_out = round_c(
                input_data.astype(compute_type) / out_scale * bnt)
            self.attrs['round_type'] = 1
        quant_data = np.clip(round_out, -bnt - 1, bnt)
        if dequantize:
            output_data = (quant_data * out_scale / bnt).astype(dtype)
            self.op_type = 'fake_quantize_dequantize_moving_average_abs_max'
        else:
            output_data = quant_data.astype(dtype)
        self.inputs = {
            'X': input_data,
            'InScale': in_scale,
            'InAccum': in_accum,
            'InState': in_state
        }
        self.outputs = {
            'Out': output_data,
            'OutAccum': out_accum,
            'OutState': out_state,
            'OutScale': out_scale
        }
        self.dtype = dtype
        self.check_output()
        if with_gradient:
            gradient = [
                np.ones(input_data.shape) / np.product(input_data.shape)
            ]
            self.check_grad(['X'], 'Out', user_defined_grads=gradient)

    def test_fake_quantize_moving_average_abs_max(self):
        self._fake_quantize_moving_average_abs_max(np.float32, (8, 16, 7, 7),
                                                   np.random.random)

    def test_fake_quantize_moving_average_abs_max_float16(self):
        self._fake_quantize_moving_average_abs_max(np.float16, (8, 16, 7, 7),
                                                   np.random.random)

    def test_fake_quantize_moving_average_abs_max_round1(self):
        self._fake_quantize_moving_average_abs_max(
            np.float32, (8, 16, 7, 7),
            np.random.random,
            round_type='TiesAwayFromZero')

    def test_fake_quantize_dequantize_moving_average_abs_max(self):
        self._fake_quantize_moving_average_abs_max(np.float32, (8, 16, 7, 7),
                                                   np.random.random,
                                                   dequantize=True,
                                                   with_gradient=True)


class TestFakeQuantizeDequantizeAbsMaxOp(OpTest):

    def setUp(self):
        self.op_type = 'fake_quantize_dequantize_abs_max'
        self.attrs = {'bit_length': 8}

    def _fake_quantize_dequantize_abs_max(self,
                                          dtype,
                                          input_shape,
                                          distribution,
                                          round_type='TiesToEven'):
        input_data = distribution(input_shape).astype(dtype)
        scale = np.max(np.abs(input_data)).astype(dtype)
        bnt = (1 << (self.attrs['bit_length'] - 1)) - 1
        if round_type == 'TiesToEven':
            round_out = np.round(input_data / scale * bnt)
            self.attrs['round_type'] = 0
        else:
            round_out = round_c(input_data / scale * bnt)
            self.attrs['round_type'] = 1
        output_data = np.clip(round_out, -bnt - 1, bnt) * scale / bnt
        self.inputs = {'X': input_data}
        self.outputs = {
            'Out': output_data,
            'OutScale': np.array(scale).astype(dtype)
        }
        self.dtype = dtype
        self.check_output()
        gradient = [np.ones(input_data.shape) / np.product(input_data.shape)]
        self.check_grad(['X'], 'Out', user_defined_grads=gradient)

    def test_fake_quantize_dequantize_abs_max(self):
        self._fake_quantize_dequantize_abs_max(np.float32, (124, 240),
                                               np.random.random)

    def test_fake_quantize_dequantize_abs_max_round1(self):
        self._fake_quantize_dequantize_abs_max(np.float32, (124, 240),
                                               np.random.random,
                                               round_type='TiesAwayFromZero')


class TestChannelWiseFakeQuantizeDequantizeAbsMaxOp(OpTest):

    def setUp(self):
        self.op_type = 'fake_channel_wise_quantize_dequantize_abs_max'
        self.attrs = {'bit_length': 8}

    def _fake_channel_wise_quantize_dequantize_abs_max(self,
                                                       dtype,
                                                       input_shape,
                                                       quant_axis,
                                                       distribution,
                                                       round_type='TiesToEven'):
        assert quant_axis in [0, 1], 'quant_axis should be 0 or 1.'
        input_data = distribution(input_shape).astype(dtype)
        compute_type = get_compute_type(dtype)
        bnt = (1 << (self.attrs['bit_length'] - 1)) - 1
        output_data = input_data.copy().astype(compute_type)
        compute_axis = tuple(i for i in range(len(input_shape))
                             if i != quant_axis)
        scale_broadcast = np.amax(input_data, axis=compute_axis, keepdims=True)
        if round_type == 'TiesToEven':
            round_out = np.round(bnt * output_data / scale_broadcast)
            self.attrs['round_type'] = 0
        else:
            round_out = round_c(bnt * output_data / scale_broadcast)
            self.attrs['round_type'] = 1
        output_data = np.clip(round_out, -bnt - 1, bnt) * scale_broadcast / bnt
        if quant_axis == 1:
            scale_broadcast = np.transpose(scale_broadcast,
                                           (1, ) + compute_axis)
        scale = scale_broadcast.reshape(input_shape[quant_axis], -1)[:, 0]
        self.inputs = {'X': input_data}
        self.outputs = {'Out': output_data, 'OutScale': scale}
        self.dtype = dtype
        self.attrs['quant_axis'] = quant_axis
        self.check_output()
        gradient = [np.ones(input_data.shape) / np.product(input_data.shape)]
        self.check_grad(['X'], 'Out', user_defined_grads=gradient)

    def test_channel_wise_fake_quant_dequant_abs_max(self):
        input_shape_quant_axis_options = [[(3, 4, 64, 64), 0],
                                          [(15, 20, 5, 5), 1], [(30, 15), 0],
                                          [(30, 15), 1]]
        round_type_options = ['TiesToEven', 'TiesAwayFromZero']
        for input_shape_quant_axis, round_type in itertools.product(
                input_shape_quant_axis_options, round_type_options):
            input_shape, quant_axis = input_shape_quant_axis
            with self.subTest(input_shape=input_shape,
                              quant_axis=quant_axis,
                              round_type=round_type):
                self._fake_channel_wise_quantize_dequantize_abs_max(
                    np.float32,
                    input_shape,
                    quant_axis,
                    np.random.random,
                    round_type=round_type)


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 channel_wise_quantize_max_abs(x, quant_bit=8, quant_axis=0):
    assert quant_axis in [0, 1], "The quant_axis should be 0 or 1."
    scales = []
    y = x.copy()
    max_range = math.pow(2, quant_bit - 1) - 1
    if quant_axis == 0:
        for i in range(x.shape[0]):
            scale = np.max(np.abs(x[i])).astype("float32")
            scales.append(scale)
            y[i] = np.round(x[i] * max_range / scale)
    elif quant_axis == 1:
        for i in range(x.shape[1]):
            scale = np.max(np.abs(x[:, i])).astype("float32")
            scales.append(scale)
            y[:, i] = np.round(x[:, i] * max_range / scale)
    return y, scales


class TestChannelWiseQuantizeOp(OpTest):

    def set_args(self):
        self.bit_length = 8
        self.data_type = "float32"
        self.quant_axis = 0

    def setUp(self):
        self.set_args()
        self.op_type = "quantize_linear"
        x = np.random.randn(4, 3, 64, 64).astype(self.data_type)
        yq, scale = channel_wise_quantize_max_abs(x, self.bit_length,
                                                  self.quant_axis)
        scale = np.array(scale).astype(self.data_type)
        zero_point = np.zeros(scale.shape, dtype="int32")

        self.inputs = {'X': x, 'Scale': scale, 'ZeroPoint': zero_point}
        self.attrs = {
            'bit_length': self.bit_length,
            'quant_axis': self.quant_axis
        }
        self.outputs = {'Y': yq}

    def test_check_output(self):
        self.check_output()


class TestChannelWiseQuantizeOp1(TestChannelWiseQuantizeOp):

    def set_args(self):
        self.bit_length = 8
        self.data_type = "float32"
        self.quant_axis = 1


class TestChannelWiseQuantizeOpTrain(OpTest):

    def set_args(self):
        self.bit_length = 8
        self.data_type = "float32"
        self.quant_axis = 0
        self.is_test = False

    def setUp(self):
        self.set_args()
        self.op_type = "quantize_linear"
        x = np.random.randn(4, 3, 64, 64).astype(self.data_type)
        yq, scale = channel_wise_quantize_max_abs(x, self.bit_length,
                                                  self.quant_axis)
        scale = np.array(scale).astype(self.data_type)
        zero_point = np.zeros(scale.shape, dtype="int32")

        self.inputs = {'X': x, 'Scale': scale, 'ZeroPoint': zero_point}
        self.attrs = {
            'bit_length': self.bit_length,
            'quant_axis': self.quant_axis,
            'is_test': self.is_test
        }
        self.outputs = {'Y': yq, 'OutScale': scale}

    def test_check_output(self):
        self.check_output()


class TestquantizeOp(OpTest):

    def set_args(self):
        self.bit_length = 8
        self.quant_axis = -1
        self.max_range = math.pow(2, self.bit_length - 1) - 1
        self.data_type = "float32"

    def setUp(self):
        self.set_args()
        self.op_type = "quantize_linear"
        x = np.random.randn(31, 65).astype(self.data_type)
        yq, scale = quantize_max_abs(x, self.max_range)
        scale = np.array(scale).astype(self.data_type)
        zero_point = np.zeros(scale.shape, dtype="int32")

        self.inputs = {'X': x, 'Scale': scale, 'ZeroPoint': zero_point}
        self.attrs = {
            'bit_length': self.bit_length,
            'quant_axis': self.quant_axis,
        }
        self.outputs = {'Y': yq}

    def test_check_output(self):
        self.check_output()


class TestquantizeOpTrain(TestquantizeOp):

    def set_args(self):
        self.bit_length = 8
        self.quant_axis = -1
        self.max_range = math.pow(2, self.bit_length - 1) - 1
        self.data_type = "float32"
        self.is_test = False

    def setUp(self):
        self.set_args()
        self.op_type = "quantize_linear"
        x = np.random.randn(31, 65).astype(self.data_type)
        yq, scale = quantize_max_abs(x, self.max_range)
        scale = np.array(scale).astype(self.data_type)
        zero_point = np.zeros(scale.shape, dtype="int32")

        self.inputs = {'X': x, 'Scale': scale, 'ZeroPoint': zero_point}
        self.attrs = {
            'bit_length': self.bit_length,
            'quant_axis': self.quant_axis,
            'is_test': self.is_test
        }
        self.outputs = {'Y': yq, 'OutScale': scale}

    def test_check_output(self):
        self.check_output()


if __name__ == '__main__':
    unittest.main()