post_training_quantization.py

#   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.

import os
import re
import math
import shutil
import logging
import numpy as np

try:
    from tqdm import tqdm
except:
    from .utils import tqdm
from inspect import isgeneratorfunction
from .... import io
from .... import core
from .... import reader
from .... import framework
from .... import unique_name
from ....executor import global_scope, Executor
from ....framework import IrGraph
from ....log_helper import get_logger
from .quantization_pass import (
    QuantizationTransformPass,
    QuantizationTransformPassV2,
    QuantizationFreezePass,
    QuantWeightPass,
    AddQuantDequantPass,
    AddQuantDequantPassV2,
)
from .cal_kl_threshold import cal_kl_threshold
from .adaround import run_adaround
from . import utils

__all__ = [
    'PostTrainingQuantization',
    'WeightQuantization',
    'PostTrainingQuantizationProgram',
]

_logger = get_logger(
    __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s'
)


def _all_persistable_var_names(program):
    persistable_var_names = []
    for var in program.list_vars():
        if var.persistable:
            persistable_var_names.append(var.name)
    return persistable_var_names


def _remove_unused_var_nodes(graph):
    all_used_vars = set()
    ops = graph.all_op_nodes()
    for op_node in ops:
        for input_node in op_node.inputs:
            all_used_vars.add(input_node)
        for output_node in op_node.outputs:
            all_used_vars.add(output_node)

    all_used_vars = {n.node for n in all_used_vars}
    all_unused_vars = {
        n
        for n in filter(
            lambda node: node.node not in all_used_vars, graph.all_var_nodes()
        )
    }
    graph.safe_remove_nodes(all_unused_vars)
    return graph


def _remove_ctrl_vars(graph):
    remove_ctr_vars = set()
    for node in graph.all_var_nodes():
        if node.is_ctrl_var():
            remove_ctr_vars.add(node)
    graph.safe_remove_nodes(remove_ctr_vars)
    return graph


def _apply_pass(
    scope, graph, pass_name, attrs=None, attr_values=None, debug=False
):
    ir_pass = core.get_pass(pass_name)
    cpp_graph = graph.graph
    if not cpp_graph.has('__param_scope__'):
        cpp_graph.set_not_owned('__param_scope__', scope)
    if attrs:
        assert attr_values and len(attrs) == len(
            attr_values
        ), "Different number of pass attributes and their values."
        for attr, value in zip(attrs, attr_values):
            ir_pass.set(attr, value)
    ir_pass.apply(cpp_graph)
    if debug:
        graph.draw('.', 'qat_fp32_{}'.format(pass_name), graph.all_op_nodes())
    _remove_unused_var_nodes(graph)
    return graph


class PostTrainingQuantization:
    """
    Utilizing post training quantization methon to quantize the FP32 model,
    and it uses calibrate data to get the quantization information for all
    quantized variables.
    """

    def __init__(
        self,
        executor,
        model_dir,
        scope=None,
        model_filename=None,
        params_filename=None,
        batch_generator=None,
        sample_generator=None,
        data_loader=None,
        batch_size=10,
        batch_nums=None,
        algo="KL",
        hist_percent=0.99999,
        quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
        round_type='round',
        learning_rate=0.001,
        is_full_quantize=False,
        bias_correction=False,
        activation_bits=8,
        weight_bits=8,
        activation_quantize_type='range_abs_max',
        weight_quantize_type='channel_wise_abs_max',
        onnx_format=False,
        freeze_model=True,
        optimize_model=False,
        is_use_cache_file=False,
        skip_tensor_list=None,
        same_scale_tensor_list=None,
        cache_dir=None,
        scale_dict=None,
        return_graph=False,
    ):
        '''
        Constructor.

        Args:
            executor(fluid.Executor): The executor to load, run and save the
                quantized model.
            scope(fluid.Scope, optional): The scope of the program, use it to load
                and save variables. If scope=None, get scope by global_scope().
            model_dir(str): The path of the fp32 model that will be quantized,
                and the model and params files are under the path.
            model_filename(str, optional): The name of file to load the inference
                program. If it is None, the default filename '__model__' will
                be used. Default is 'None'.
            params_filename(str, optional): The name of file to load all parameters.
                When all parameters were saved in a single binary file, set it
                as the real filename. If parameters were saved in separate files,
                set it as 'None'. Default is 'None'.
            batch_generator(Python Generator): The batch generator provides
                calibrate data for DataLoader, and it returns a batch every
                time. Note that, sample_generator and batch_generator, only one
                should be set. Beisdes, batch_generator supports lod tensor.
            sample_generator(Python Generator): The sample generator provides
                calibrate data for DataLoader, and it only returns a sample every
                time. Note that, sample_generator and batch_generator, only one
                should be set. Beisdes, sample_generator dose not support lod tensor.
            data_loader(Python Generator, Paddle.io.DataLoader, optional): The
                Generator or Dataloader provides calibrate data, and it could
                return a batch every time.
            batch_size(int, optional): The batch size of DataLoader. Default is 10.
            batch_nums(int, optional): If batch_nums is not None, the number of
                calibrate data is batch_size*batch_nums. If batch_nums is None, use
                all data provided by sample_generator as calibrate data.
            algo(str, optional): If algo='KL', use KL-divergenc method to
                get the KL threshold for quantized activations and get the abs_max
                value for quantized weights. If algo='abs_max', get the abs max
                value for activations and weights. If algo= 'min_max', get the min
                and max value for quantized activations and weights. If algo='avg',
                get the average value among the max values for activations. If
                algo= 'hist', get the value of 'hist_percent' quantile as the threshold.
                If algo='mse', get the value which makes the quantization mse loss
                minimal. Default is KL.
            hist_percent(float, optional): The threshold of algo 'hist' for activations.
                Default is 0.99999.
            quantizable_op_type(list[str], optional): List the type of ops
                that will be quantized. Default is ["conv2d", "depthwise_conv2d",
                "mul"].
            round_type(str, optional): The method of converting the quantized weights
                value float->int. Currently supports ['round', 'adaround'] methods.
                Default is `round`, which is rounding nearest to the integer.
                'adaround' is refer to https://arxiv.org/abs/2004.10568.
            learning_rate(float, optional): The learning rate of adaround method.
            is_full_quantized(bool, optional): If set is_full_quantized as True,
                apply quantization to all supported quantizable op type. If set
                is_full_quantized as False, only apply quantization to the op type
                according to the input quantizable_op_type.
            bias_correction(bool, optional): If set as True, use the bias correction
                method of https://arxiv.org/abs/1810.05723. Default is False.
            activation_bits(int): quantization bit number for activation.
            weight_bits(int, optional): quantization bit number for weights.
            activation_quantize_type(str): quantization type for activation,
                now support 'range_abs_max', 'moving_average_abs_max' and 'abs_max'.
                This param only specifies the fake ops in saving quantized model.
                If it is 'range_abs_max' or 'moving_average_abs_max', we save the scale
                obtained by post training quantization in fake ops. Note that, if it
                is 'abs_max', the scale will not be saved in fake ops.
            weight_quantize_type(str): quantization type for weights,
                support 'abs_max' and 'channel_wise_abs_max'. This param only specifies
                the fake ops in saving quantized model, and we save the scale obtained
                by post training quantization in fake ops. Compared to 'abs_max',
                the model accuracy is usually higher when it is 'channel_wise_abs_max'.
            onnx_format(bool): Whether to export the quantized model with format of ONNX.
                Default is False.
            freeze_model(bool): Whether to convert quantized and trained ``program`` to final
                quantized ``program``. Default: True.
            skip_tensor_list(list): List of skip quant tensor name. Default: None.
            same_scale_tensor_list(list(list)): The list of tensor keep same scale in the outermost
                list, the final scale about every list is the max of the scale in the list
                of tensor. Default: None.
            optimize_model(bool, optional): If set optimize_model as True, it applies
                some passes to the model before quantization, and it supports
                `conv2d/depthwise_conv2d + bn` pass so far. Some targets require the
                weights are quantized by tensor-wise method, which means the weights
                scale for all channel are the same. However, if fuse
                `conv2d/depthwise_conv2d + bn`, the weights scale for all channel will
                be different. In address this problem, fuse the pattern before
                quantization. Default False.
            is_use_cache_file(bool, optional): This param is deprecated.
            cache_dir(str, optional): This param is deprecated.
        Returns:
            None

        Examples:
        .. code-block:: python
            import paddle.fluid as fluid
            from paddle.fluid.contrib.slim.quantization import PostTrainingQuantization

            exe = fluid.Executor(fluid.CPUPlace())
            model_dir = path/to/fp32_model_params
            # set model_filename as None when the filename is __model__,
            # otherwise set it as the real filename
            model_filename = None
            # set params_filename as None when all parameters were saved in
            # separate files, otherwise set it as the real filename
            params_filename = None
            save_model_path = path/to/save_model_path
            # prepare the sample generator according to the model, and the
            # sample generator must return a sample every time. The reference
            # document: https://www.paddlepaddle.org.cn/documentation/docs/zh
            # /user_guides/howto/prepare_data/use_py_reader.html
            sample_generator = your_sample_generator
            batch_size = 10
            batch_nums = 10
            algo = "KL"
            quantizable_op_type = ["conv2d", "depthwise_conv2d", "mul"]
            ptq = PostTrainingQuantization(
                        executor=exe,
                        sample_generator=sample_generator,
                        model_dir=model_dir,
                        model_filename=model_filename,
                        params_filename=params_filename,
                        batch_size=batch_size,
                        batch_nums=batch_nums,
                        algo=algo,
                        quantizable_op_type=quantizable_op_type)
            ptq.quantize()
            ptq.save_quantized_model(save_model_path)
        '''

        self._support_activation_quantize_type = [
            'range_abs_max',
            'moving_average_abs_max',
            'abs_max',
        ]
        self._support_weight_quantize_type = ['abs_max', 'channel_wise_abs_max']
        self._support_algo_type = [
            'KL',
            'hist',
            'avg',
            'mse',
            'emd',
            'abs_max',
            'min_max',
            'ptf',
        ]
        assert round_type in ['adaround', 'round']
        self._round_type = round_type
        self._learning_rate = learning_rate
        self._dynamic_quantize_op_type = ['lstm']
        self._support_quantize_op_type = list(
            set(
                utils._weight_supported_quantizable_op_type
                + utils._act_supported_quantizable_op_type
                + self._dynamic_quantize_op_type
            )
        )

        # Check inputs
        assert executor is not None, "The executor cannot be None."
        assert any(
            [gen is not None]
            for gen in [sample_generator, batch_generator, data_loader]
        ), (
            "The sample_generator, batch_generator "
            "and data_loader cannot be None in the same time."
        )
        if data_loader is not None:
            assert isinstance(
                data_loader,
                (
                    io.DataLoader,
                    type(isgeneratorfunction),
                    reader.GeneratorLoader,
                ),
            ), "data_loader only accepts `paddle.io.DataLoader` or Generator instance."
        assert batch_size > 0, "The batch_size should be greater than 0."
        assert (
            algo in self._support_algo_type
        ), "The algo should be KL, hist, mse, avg, abs_max, min_max or ptf."
        assert (
            activation_quantize_type in self._support_activation_quantize_type
        ), "The activation_quantize_type ({}) should in ({}).".format(
            activation_quantize_type, self._support_activation_quantize_type
        )
        assert (
            weight_quantize_type in self._support_weight_quantize_type
        ), "The weight_quantize_type ({}) shoud in ({}).".format(
            weight_quantize_type, self._support_weight_quantize_type
        )

        # Save input params
        self._bias_correction = bias_correction
        self._executor = executor
        self._scope = global_scope() if scope is None else scope
        self._model_dir = model_dir
        self._model_filename = model_filename
        self._params_filename = params_filename
        self._sample_generator = sample_generator
        self._batch_generator = batch_generator
        self._batch_size = batch_size
        self._batch_nums = batch_nums
        self._algo = algo
        self._hist_percent = hist_percent
        self._activation_bits = activation_bits
        self._weight_bits = weight_bits
        self._activation_quantize_type = activation_quantize_type
        self._weight_quantize_type = weight_quantize_type
        self._onnx_format = onnx_format
        self._clip_extra = True if self._onnx_format else False
        self._skip_tensor_list = skip_tensor_list
        self._is_full_quantize = is_full_quantize
        if is_full_quantize:
            self._quantizable_op_type = self._support_quantize_op_type
        else:
            self._quantizable_op_type = quantizable_op_type
            for op_type in self._quantizable_op_type:
                assert op_type in self._support_quantize_op_type, (
                    op_type + " is not supported for quantization."
                )
        self._optimize_model = optimize_model

        # Define variables
        self._place = self._executor.place
        self._program = None
        self._feed_list = None
        self._fetch_list = None
        self._data_loader = data_loader

        self._out_scale_op_list = utils.QUANT_SUPPORTED_OP_TYPE_LIST
        self._quantized_weight_var_name = set()
        self._quantized_act_var_name = set()
        self._weight_op_pairs = {}
        # The vars for alog = KL or hist
        self._sampling_act_abs_min_max = {}
        self._sampling_act_histogram = {}
        self._sampling_data = {}
        self._quantized_var_threshold = {}
        self._histogram_bins = 2048
        # The vars for algo = min_max
        self._quantized_var_min = {}
        self._quantized_var_max = {}
        # The vars for algo = avg
        self._quantized_var_avg = {}
        # The best loss of algo = mse
        self._best_calibration_loss = {}
        # The threshold for algo = abs_max, mse or avg
        self._quantized_threshold = {}
        self._same_scale_tensor_list = same_scale_tensor_list
        self._freeze_model = freeze_model
        self._scale_dict = scale_dict
        self._return_graph = return_graph
        self.FLAG = False
        if self._program is not None:
            self.FLAG = True

    def quantize(self):
        '''
        Load the FP32 model, and use the calibrate data to calculate the forward-stage.
        Based on the sample data, we can get the quantization information, and obtain
        the final quantized model.

        Args:
            None
        Returns:
            the program of quantized model.
        '''
        self._load_model_data()
        self._collect_target_varnames()
        self._set_activation_persistable()

        if self._algo in ["KL", "hist"]:
            batch_id = 0
            with tqdm(
                total=self._batch_nums,
                bar_format='Preparation stage, Run batch:|{bar}| {n_fmt}/{total_fmt}',
                ncols=80,
            ) as t:
                for data in self._data_loader():
                    self._executor.run(
                        program=self._program,
                        feed=data,
                        fetch_list=self._fetch_list,
                        return_numpy=False,
                        scope=self._scope,
                    )
                    self._collect_activation_abs_min_max()
                    batch_id += 1
                    t.update()
                    if self._batch_nums and batch_id >= self._batch_nums:
                        break
            self._init_sampling_act_histogram()

        batch_id = 0
        with tqdm(
            total=self._batch_nums,
            bar_format='Sampling stage, Run batch:|{bar}| {n_fmt}/{total_fmt}',
            ncols=80,
        ) as t:
            for data in self._data_loader():
                self._executor.run(
                    program=self._program,
                    feed=data,
                    fetch_list=self._fetch_list,
                    return_numpy=False,
                    scope=self._scope,
                )
                self._sampling()
                batch_id += 1
                t.update()
                if self._batch_nums and batch_id >= self._batch_nums:
                    break

        if self._algo == 'avg':
            for var_name in self._quantized_act_var_name:
                self._quantized_threshold[var_name] = np.array(
                    self._quantized_var_avg[var_name]
                ).mean()
        if self._algo in ["KL", "hist"]:
            self._calculate_kl_hist_threshold()

        if self._round_type == 'adaround':
            self._adaround_apply()

        self._reset_activation_persistable()

        if self._algo == 'min_max':
            self._save_input_threhold()
        else:
            self._update_program()

        # save out_threshold for quantized ops.
        if not self.FLAG:
            self._save_output_threshold()

        if any(
            op_type in self._quantizable_op_type
            for op_type in self._dynamic_quantize_op_type
        ):
            self._collect_dynamic_quantize_op_threshold(
                self._dynamic_quantize_op_type
            )

        utils.move_persistable_var_to_global_block(self._program)

        if not self._return_graph:
            return self._program
        else:
            main_graph = IrGraph(core.Graph(self._program.desc), for_test=True)
            return main_graph

    def _adaround_apply(self):
        assert self._algo != "min_max", "The algo should not be min_max."
        if self._algo in ["KL", "hist"]:
            scale_dict = self._quantized_var_threshold
        else:
            scale_dict = self._quantized_threshold
        run_adaround(
            self._data_loader,
            self._program,
            self._fetch_list,
            self._executor,
            self._scope,
            self._place,
            self._quantized_op_pairs,
            self._weight_op_pairs,
            scale_dict,
            num_iterations=self._batch_nums,
            bias_correction=self._bias_correction,
            lr=self._learning_rate,
        )

    def save_quantized_model(
        self, save_model_path, model_filename=None, params_filename=None
    ):
        '''
        Save the quantized model to the disk.

        Args:
            save_model_path(str): The path to save the quantized model.
            model_filename(str, optional): If the model_filename is None,
                save the model to '__model__'. Otherwise, save the model
                to the specified filename. Default: None.
            params_filename(str, optional): If the params_filename is None,
                save params to separted files. Otherwise, save all params
                to the specified filename.
        Returns:
            None
        '''
        io.save_inference_model(
            dirname=save_model_path,
            model_filename=model_filename,
            params_filename=params_filename,
            feeded_var_names=self._feed_list,
            target_vars=self._fetch_list,
            executor=self._executor,
            main_program=self._program,
            clip_extra=self._clip_extra,
        )
        _logger.info("The quantized model is saved in " + save_model_path)

    def _load_model_data(self):
        '''
        Load model and set data loader.
        '''
        if self._program is None:
            _logger.info("Load model and set data loader ...")
            [
                self._program,
                self._feed_list,
                self._fetch_list,
            ] = io.load_inference_model(
                dirname=self._model_dir,
                executor=self._executor,
                model_filename=self._model_filename,
                params_filename=self._params_filename,
            )

        if self._optimize_model:
            self._optimize_fp32_model()

        feed_vars = [
            framework._get_var(str(var_name), self._program)
            for var_name in self._feed_list
        ]

        if self._data_loader is not None:
            self._batch_nums = (
                self._batch_nums if self._batch_nums else len(self._data_loader)
            )
            return
        self._data_loader = io.DataLoader.from_generator(
            feed_list=feed_vars, capacity=3 * self._batch_size, iterable=True
        )
        if self._sample_generator is not None:
            self._data_loader.set_sample_generator(
                self._sample_generator,
                batch_size=self._batch_size,
                drop_last=True,
                places=self._place,
            )
        elif self._batch_generator is not None:
            self._data_loader.set_batch_generator(
                self._batch_generator, places=self._place
            )
        self._batch_nums = (
            self._batch_nums
            if self._batch_nums
            else len(list(self._data_loader))
        )

    def _optimize_fp32_model(self):
        '''
        Fuse the `conv2d/depthwise_conv2d + bn` in FP32 model.
        '''
        _logger.info("Optimize FP32 model ...")
        graph = IrGraph(core.Graph(self._program.desc), for_test=True)
        graph = _remove_ctrl_vars(graph)
        graph = _apply_pass(self._scope, graph, 'conv_bn_fuse_pass')
        graph = _apply_pass(self._scope, graph, 'depthwise_conv_bn_fuse_pass')
        graph = _apply_pass(self._scope, graph, 'conv_transpose_bn_fuse_pass')
        graph = _apply_pass(self._scope, graph, 'conv_eltwiseadd_bn_fuse_pass')
        graph = _apply_pass(
            self._scope, graph, 'depthwise_conv_eltwiseadd_bn_fuse_pass'
        )

        self._program = graph.to_program()

    def _collect_target_varnames(self):
        '''
        Collect the variable names for sampling, and set activation
        variables to be persistable.
        '''
        # TODO(juncaipeng), consider the name_scope of skip_quant
        _logger.info("Collect quantized variable names ...")
        self._quantized_op_pairs = {}

        def collect_var_name(var_name_list, persistable_var_names, op_type):
            for var_name in var_name_list:
                if var_name in persistable_var_names:
                    self._quantized_weight_var_name.add(var_name)
                    self._weight_op_pairs[var_name] = op_type
                else:
                    self._quantized_act_var_name.add(var_name)

        persistable_var_names = _all_persistable_var_names(self._program)
        for block_id in range(len(self._program.blocks)):
            for op in self._program.blocks[block_id].ops:
                # skip quant form self._skip_tensor_list
                if self._skip_tensor_list is not None:
                    for inp_name in utils._get_op_input_var_names(op):
                        if inp_name in self._skip_tensor_list:
                            op._set_attr("op_namescope", "skip_quant")

                op_type = op.type
                if (
                    self._is_full_quantize
                    and op_type not in self._quantizable_op_type
                ):
                    _logger.warning(
                        op_type + " is not supported for quantization."
                    )
                # For quantized ops, sample inputs and outputs
                if op_type in self._quantizable_op_type:
                    collect_var_name(
                        utils._get_op_input_var_names(op),
                        persistable_var_names,
                        op_type,
                    )
                    collect_var_name(
                        utils._get_op_output_var_names(op),
                        persistable_var_names,
                        op_type,
                    )
                    # collect quanted op output var name
                    for out_var_name in utils._get_op_output_var_names(op):
                        for in_var_name in utils._get_op_input_var_names(op):
                            if in_var_name in persistable_var_names:
                                self._quantized_op_pairs[
                                    in_var_name
                                ] = out_var_name
                # For other op, only sample output scale
                elif op_type in self._out_scale_op_list:
                    collect_var_name(
                        utils._get_op_output_var_names(op),
                        persistable_var_names,
                        op_type,
                    )

    def _set_activation_persistable(self):
        '''
        Set activation variables to be persistable, so can obtain
        the tensor data in sample_data
        '''
        for var in self._program.list_vars():
            if var.name in self._quantized_act_var_name:
                var.persistable = True

    def _reset_activation_persistable(self):
        '''
        Reset activations to be not persistable.
        '''
        for var in self._program.list_vars():
            if var.name in self._quantized_act_var_name:
                var.persistable = False
                self._scope.find_var(var.name).get_tensor()._clear()

    def _sampling(self):
        '''
        Sample the min/max, abs_max or histogram in every iterations.
        '''
        if self._algo == "abs_max":
            self._sample_abs_max()
        elif self._algo == "avg":
            self._sample_avg()
        elif self._algo == "min_max":
            self._sample_min_max()
        elif self._algo == "mse":
            self._sample_mse()
        elif self._algo == "emd":
            self._sample_emd()
        elif self._algo == "ptf":
            self._sample_ptf()
        elif self._algo in ["KL", "hist"]:
            self._sample_histogram()

    def _sample_mse(self):
        if self._quantized_threshold == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    abs_max_value = float(np.max(np.abs(var_tensor)))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    abs_max_value = []
                    if (
                        self._weight_op_pairs[var_name]
                        in utils._channelwise_quant_axis1_ops
                    ):
                        for i in range(var_tensor.shape[1]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[:, i])))
                            )
                    else:
                        for i in range(var_tensor.shape[0]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[i])))
                            )
                self._quantized_threshold[var_name] = abs_max_value
        _logger.info("MSE searching stage ...")
        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            var_tensor = var_tensor.flatten()
            abs_max_value = float(np.max(np.abs(var_tensor)))
            abs_max_value = 1e-8 if abs_max_value == 0.0 else abs_max_value
            s = 0.3
            if var_name not in self._best_calibration_loss:
                self._best_calibration_loss[var_name] = float('inf')
            while s <= 1.0:
                scale = s * abs_max_value
                s += 0.02
                bins = 2 ** (self._activation_bits - 1) - 1
                if self._onnx_format:
                    quant_var = np.clip(
                        np.round(var_tensor / scale * bins), -bins - 1, bins
                    )
                    quant_dequant_var = quant_var / bins * scale
                else:
                    quant_dequant_var = (
                        np.round(np.clip(var_tensor, 0.0, scale) / scale * bins)
                        / bins
                        * scale
                    )
                mse_loss = ((var_tensor - quant_dequant_var) ** 2).mean()
                if mse_loss <= self._best_calibration_loss[var_name]:
                    self._best_calibration_loss[var_name] = mse_loss
                    self._quantized_threshold[var_name] = scale

    def _sample_emd(self):
        if self._quantized_threshold == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    abs_max_value = float(np.max(np.abs(var_tensor)))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    abs_max_value = []
                    if (
                        self._weight_op_pairs[var_name]
                        in utils._channelwise_quant_axis1_ops
                    ):
                        for i in range(var_tensor.shape[1]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[:, i])))
                            )
                    else:
                        for i in range(var_tensor.shape[0]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[i])))
                            )
                self._quantized_threshold[var_name] = abs_max_value
        _logger.info("EMD searching stage ...")
        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            var_tensor = var_tensor.flatten()
            abs_max_value = float(np.max(np.abs(var_tensor)))
            abs_max_value = 1e-8 if abs_max_value == 0.0 else abs_max_value
            s = 0.3
            if var_name not in self._best_calibration_loss:
                self._best_calibration_loss[var_name] = float('inf')
            while s <= 1.0:
                scale = s * abs_max_value
                s += 0.02
                bins = 2 ** (self._activation_bits - 1) - 1
                if self._onnx_format:
                    quant_var = np.clip(
                        np.round(var_tensor / scale * bins), -bins - 1, bins
                    )
                    quant_dequant_var = quant_var / bins * scale
                else:
                    quant_dequant_var = (
                        np.round(np.clip(var_tensor, 0.0, scale) / scale * bins)
                        / bins
                        * scale
                    )
                emd_loss = np.abs(
                    np.mean(var_tensor) - np.mean(quant_dequant_var)
                ) + np.abs(np.std(var_tensor) - np.std(quant_dequant_var))
                if emd_loss <= self._best_calibration_loss[var_name]:
                    self._best_calibration_loss[var_name] = emd_loss
                    self._quantized_threshold[var_name] = scale

    def _sample_avg(self):
        if self._quantized_threshold == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    abs_max_value = float(np.max(np.abs(var_tensor)))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    abs_max_value = []
                    if (
                        self._weight_op_pairs[var_name]
                        in utils._channelwise_quant_axis1_ops
                    ):
                        for i in range(var_tensor.shape[1]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[:, i])))
                            )
                    else:
                        for i in range(var_tensor.shape[0]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[i])))
                            )
                self._quantized_threshold[var_name] = abs_max_value

        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            abs_max_value = float(np.max(np.abs(var_tensor)))
            if var_name not in self._quantized_var_avg:
                self._quantized_var_avg[var_name] = []
            abs_avg_value = float(
                np.mean(
                    np.max(
                        np.abs(var_tensor.reshape(var_tensor.shape[0], -1)),
                        axis=(1),
                    )
                )
            )
            self._quantized_var_avg[var_name].append(abs_avg_value)
            continue

    def _sample_abs_max(self):
        if self._quantized_threshold == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    abs_max_value = float(np.max(np.abs(var_tensor)))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    abs_max_value = []
                    if (
                        self._weight_op_pairs[var_name]
                        in utils._channelwise_quant_axis1_ops
                    ):
                        for i in range(var_tensor.shape[1]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[:, i])))
                            )
                    else:
                        for i in range(var_tensor.shape[0]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[i])))
                            )
                self._quantized_threshold[var_name] = abs_max_value

        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            abs_max_value = float(np.max(np.abs(var_tensor)))
            if (var_name not in self._quantized_threshold) or (
                abs_max_value > self._quantized_threshold[var_name]
            ):
                self._quantized_threshold[var_name] = abs_max_value

    def _sample_min_max(self):
        if self._quantized_var_min == {} and self._quantized_var_max == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    min_value = float(np.min(var_tensor))
                    max_value = float(np.max(var_tensor))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    min_value = []
                    max_value = []
                    if (
                        self._weight_op_pairs[var_name]
                        in utils._channelwise_quant_axis1_ops
                    ):
                        for i in range(var_tensor.shape[1]):
                            min_value.append(float(np.min(var_tensor[:, i])))
                            max_value.append(float(np.max(var_tensor[:, i])))
                    else:
                        for i in range(var_tensor.shape[0]):
                            min_value.append(float(np.min(var_tensor[i])))
                            max_value.append(float(np.max(var_tensor[i])))
                self._quantized_var_min[var_name] = min_value
                self._quantized_var_max[var_name] = max_value

        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            min_value = float(np.min(var_tensor))
            max_value = float(np.max(var_tensor))
            if (var_name not in self._quantized_var_min) or (
                min_value < self._quantized_var_min[var_name]
            ):
                self._quantized_var_min[var_name] = min_value
            if (var_name not in self._quantized_var_max) or (
                max_value > self._quantized_var_max[var_name]
            ):
                self._quantized_var_max[var_name] = max_value

    def _sample_histogram(self):
        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            var_tensor_abs = np.abs(var_tensor)
            bins = self._sampling_act_histogram[var_name][1]
            hist, _ = np.histogram(var_tensor_abs, bins=bins)
            self._sampling_act_histogram[var_name][0] += hist

    def _sample_ptf(self):
        """
        The following code are modified from:
        https://github.com/megvii-research/FQ-ViT/
        """
        if self._quantized_threshold == {}:
            for var_name in self._quantized_weight_var_name:
                var_tensor = utils.load_variable_data(self._scope, var_name)
                if self._weight_quantize_type == "abs_max":
                    abs_max_value = float(np.max(np.abs(var_tensor)))
                elif self._weight_quantize_type == "channel_wise_abs_max":
                    abs_max_value = []
                    if (
                        self._weight_op_pairs[var_name]
                        in utils._channelwise_quant_axis1_ops
                    ):
                        for i in range(var_tensor.shape[1]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[:, i])))
                            )
                    else:
                        for i in range(var_tensor.shape[0]):
                            abs_max_value.append(
                                float(np.max(np.abs(var_tensor[i])))
                            )
                self._quantized_threshold[var_name] = abs_max_value

        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            abs_max_value = float(np.max(np.abs(var_tensor)))
            q_max = 2 ** (self._activation_bits - 1) - 1
            scale8 = abs_max_value / q_max
            scale4 = scale8 / 2
            scale2 = scale4 / 2
            scale1 = scale2 / 2
            quant_dequant_var_scale1 = (
                np.clip(np.round(var_tensor / scale1), 0, q_max) * scale1
            )
            quant_dequant_var_scale2 = (
                np.clip(np.round(var_tensor / scale2), 0, q_max) * scale2
            )
            quant_dequant_var_scale4 = (
                np.clip(np.round(var_tensor / scale4), 0, q_max) * scale4
            )
            quant_dequant_var_scale8 = (
                np.clip(np.round(var_tensor / scale8), 0, q_max) * scale8
            )
            score1 = utils.l2_loss(var_tensor, quant_dequant_var_scale1)
            score2 = utils.l2_loss(var_tensor, quant_dequant_var_scale2)
            score4 = utils.l2_loss(var_tensor, quant_dequant_var_scale4)
            score8 = utils.l2_loss(var_tensor, quant_dequant_var_scale8)
            score = [score1, score2, score4, score8]
            mask = 2 ** score.index(min(score))
            scale = scale1 * mask
            threshold = q_max * scale
            self._quantized_threshold[var_name] = threshold

    def _save_input_threhold(self):
        '''
        Save input threshold to the quantized op.
        '''
        assert (
            self._algo == "min_max"
        ), "The algo should be min_max to save input threshold."
        for block_id in range(len(self._program.blocks)):
            for op in self._program.blocks[block_id].ops:
                if op.type in self._quantizable_op_type:
                    for var_name in utils._get_op_input_var_names(op):
                        assert var_name in self._quantized_var_min
                        assert var_name in self._quantized_var_max
                        op._set_attr(
                            var_name + ".min", self._quantized_var_min[var_name]
                        )
                        op._set_attr(
                            var_name + ".max", self._quantized_var_max[var_name]
                        )
                        op._set_attr("with_quant_attr", True)

    def _collect_activation_abs_min_max(self):
        '''
        Collect the abs_min and abs_max for all activation. When algo = KL,
        get the min and max value, and then calculate the threshold.
        '''
        for var_name in self._quantized_act_var_name:
            var_tensor = utils.load_variable_data(self._scope, var_name)
            var_tensor = np.abs(var_tensor)
            min_value = float(np.min(var_tensor))
            max_value = float(np.max(var_tensor))
            if var_name not in self._sampling_act_abs_min_max:
                self._sampling_act_abs_min_max[var_name] = [
                    min_value,
                    max_value,
                ]
            else:
                if min_value < self._sampling_act_abs_min_max[var_name][0]:
                    self._sampling_act_abs_min_max[var_name][0] = min_value
                if max_value > self._sampling_act_abs_min_max[var_name][1]:
                    self._sampling_act_abs_min_max[var_name][1] = max_value

    def _init_sampling_act_histogram(self):
        '''
        Based on the min/max value, init the sampling_act_histogram.
        '''
        for var_name in self._quantized_act_var_name:
            if var_name not in self._sampling_act_histogram:
                min_val = self._sampling_act_abs_min_max[var_name][0]
                max_val = self._sampling_act_abs_min_max[var_name][1]
                hist, hist_edeges = np.histogram(
                    [], bins=self._histogram_bins, range=(min_val, max_val)
                )
                self._sampling_act_histogram[var_name] = [hist, hist_edeges]

    def _calculate_kl_hist_threshold(self):
        '''
        Calculate the KL or hist threshold of quantized variables.
        '''
        _logger.info("Calculate {} threshold ...".format(self._algo))
        assert self._algo in ["KL", "hist"], "The algo should be KL or hist."

        # Abs_max threshold for weights
        for var_name in self._quantized_weight_var_name:
            weight_data = utils.load_variable_data(self._scope, var_name)
            if self._weight_quantize_type == "abs_max":
                weight_threshold = float(np.max(np.abs(weight_data)))
            elif self._weight_quantize_type == "channel_wise_abs_max":
                weight_threshold = []
                if (
                    self._weight_op_pairs[var_name]
                    in utils._channelwise_quant_axis1_ops
                ):
                    for i in range(weight_data.shape[1]):
                        weight_threshold.append(
                            float(np.max(np.abs(weight_data[:, i])))
                        )
                else:
                    for i in range(weight_data.shape[0]):
                        weight_threshold.append(
                            float(np.max(np.abs(weight_data[i])))
                        )
            self._quantized_var_threshold[var_name] = weight_threshold

        for var_name in self._quantized_act_var_name:
            hist, hist_edeges = self._sampling_act_histogram[var_name]
            if self._algo == "KL":
                bin_width = hist_edeges[1] - hist_edeges[0]
                self._quantized_var_threshold[var_name] = cal_kl_threshold(
                    hist, bin_width, self._activation_bits
                )
            elif self._algo == "hist":
                self._quantized_var_threshold[
                    var_name
                ] = self._get_hist_scaling_factor(hist, hist_edeges)

    def _update_program(self):
        '''
        Use QuantizationTransformPass and AddQuantDequantPass to insert
        fake_quantize, fake_dequantize and fake_quant_dequant op.
        Besides, save all threshold to the scale var node.
        '''
        _logger.info("Update the program ...")
        graph = IrGraph(core.Graph(self._program.desc), for_test=True)

        # use QuantizationTransformPass to insert fake_quant/fake_dequantize op
        major_quantizable_op_types = []
        for op_type in utils._weight_supported_quantizable_op_type:
            if op_type in self._quantizable_op_type:
                major_quantizable_op_types.append(op_type)
        if not self._onnx_format:
            transform_pass = QuantizationTransformPass(
                scope=self._scope,
                place=self._place,
                weight_bits=self._weight_bits,
                activation_bits=self._activation_bits,
                activation_quantize_type=self._activation_quantize_type,
                weight_quantize_type=self._weight_quantize_type,
                quantizable_op_type=major_quantizable_op_types,
            )
        else:
            transform_pass = QuantizationTransformPassV2(
                scope=self._scope,
                place=self._place,
                weight_bits=self._weight_bits,
                activation_bits=self._activation_bits,
                activation_quantize_type=self._activation_quantize_type,
                weight_quantize_type=self._weight_quantize_type,
                quantizable_op_type=major_quantizable_op_types,
            )

        for sub_graph in graph.all_sub_graphs():
            # Insert fake_quant/fake_dequantize op must in test graph, so
            # set per graph's _for_test is True.
            sub_graph._for_test = True
            transform_pass.apply(sub_graph)

        # use AddQuantDequantPass to insert fake_quant_dequant op
        minor_quantizable_op_types = []
        for op_type in utils._act_supported_quantizable_op_type:
            if op_type in self._quantizable_op_type:
                minor_quantizable_op_types.append(op_type)
        if not self._onnx_format:
            add_quant_dequant_pass = AddQuantDequantPass(
                scope=self._scope,
                place=self._place,
                quantizable_op_type=minor_quantizable_op_types,
            )
        else:
            add_quant_dequant_pass = AddQuantDequantPassV2(
                scope=self._scope,
                place=self._place,
                quantizable_op_type=minor_quantizable_op_types,
                is_full_quantized=True,
            )

        for sub_graph in graph.all_sub_graphs():
            sub_graph._for_test = True
            add_quant_dequant_pass.apply(sub_graph)

        # save threshold to scale var node
        if self._scale_dict is None:
            if self._algo in ["KL", "hist"]:
                scale_dict = self._quantized_var_threshold
            else:
                scale_dict = self._quantized_threshold

            if self._same_scale_tensor_list is not None:
                for tensor_list in self._same_scale_tensor_list:
                    max_scale = None
                    tmp_tensor_list = []
                    for tensor_name in tensor_list:
                        if '#' in tensor_name:
                            real_tensor_name, opera, scalar = tensor_name.split(
                                '#'
                            )
                            if real_tensor_name not in scale_dict.keys():
                                continue
                            if opera == '*':
                                scale_dict[real_tensor_name] = float(
                                    scale_dict[real_tensor_name]
                                ) * float(scalar)
                            elif opera == '/':
                                scale_dict[real_tensor_name] = float(
                                    scale_dict[real_tensor_name]
                                ) / float(scalar)
                            max_scale = (
                                scale_dict[real_tensor_name]
                                if max_scale is None
                                else max(
                                    max_scale, scale_dict[real_tensor_name]
                                )
                            )
                        else:
                            if tensor_name not in scale_dict.keys():
                                continue
                            max_scale = (
                                scale_dict[tensor_name]
                                if max_scale is None
                                else max(max_scale, scale_dict[tensor_name])
                            )

                    for tensor_name in tensor_list:
                        if '#' in tensor_name:
                            real_tensor_name, opera, scalar = tensor_name.split(
                                '#'
                            )
                            if real_tensor_name not in scale_dict.keys():
                                continue
                            if opera == '*':
                                scale_dict[
                                    real_tensor_name
                                ] = max_scale / float(scalar)
                            elif opera == '/':
                                scale_dict[
                                    real_tensor_name
                                ] = max_scale * float(scalar)
                        else:
                            if tensor_name not in scale_dict.keys():
                                continue
                            scale_dict[tensor_name] = max_scale
            self._scale_dict = scale_dict

        for key, val in self._scale_dict.items():
            utils.set_variable_data(
                self._scope,
                self._place,
                key + "@scale",
                np.array([val], dtype=np.float32),
            )
            utils.set_variable_data(
                self._scope,
                self._place,
                key + ".quant_dequant@scale",
                np.array([val], dtype=np.float32),
            )

        if not self._onnx_format:
            # apply QuantizationFreezePass, and obtain the final quant model
            if self._freeze_model:
                freeze_pass = QuantizationFreezePass(
                    scope=self._scope,
                    place=self._place,
                    bias_correction=self._bias_correction,
                    weight_bits=self._weight_bits,
                    round_type=self._round_type,
                    activation_bits=self._activation_bits,
                    weight_quantize_type=self._weight_quantize_type,
                    quantizable_op_type=major_quantizable_op_types,
                )

                for sub_graph in graph.all_sub_graphs():
                    sub_graph._for_test = True
                    freeze_pass.apply(sub_graph)
        else:
            quant_weight_pass = QuantWeightPass(self._scope, self._place)
            for sub_graph in graph.all_sub_graphs():
                sub_graph._for_test = True
                quant_weight_pass.apply(sub_graph)

        self._program = graph.to_program()

    def _save_output_threshold(self):
        '''
        Save output threshold to the quantized op.
        '''
        self._calibration_scales = {}

        def save_info(
            op_node, out_var_name, threshold_map, out_info_name, quantized_type
        ):
            assert (
                out_var_name in threshold_map
            ), "The output ({}) of {} node does not have threshold.".format(
                out_var_name, op_node.type
            )
            if self._onnx_format:
                # For easy extension, every var_node set a dict to save parameters of quant.
                self._calibration_scales[var_name] = {}
                self._calibration_scales[var_name]['scale'] = threshold_map[
                    var_name
                ]
            else:
                op_node._set_attr(out_info_name, threshold_map[var_name])
                op_node._set_attr("with_quant_attr", True)
                if op_node.type in self._quantizable_op_type:
                    op._set_attr("quantization_type", quantized_type)

        def analysis_and_save_info(op_node, out_var_name):
            argname_index = utils._get_output_name_index(op_node, out_var_name)
            assert argname_index is not None, (
                out_var_name + " is not the output of the op"
            )
            if self._algo == "KL":
                # For compatibility, we save output threshold by two methods.
                save_info(
                    op_node,
                    out_var_name,
                    self._quantized_var_threshold,
                    "out_threshold",
                    "post_kl",
                )
                save_info(
                    op_node,
                    out_var_name,
                    self._quantized_var_threshold,
                    argname_index[0] + str(argname_index[1]) + "_threshold",
                    "post_kl",
                )
            elif self._algo == "hist":
                # For compatibility, we save output threshold by two methods.
                save_info(
                    op_node,
                    out_var_name,
                    self._quantized_var_threshold,
                    "out_threshold",
                    "post_hist",
                )
                save_info(
                    op_node,
                    out_var_name,
                    self._quantized_var_threshold,
                    argname_index[0] + str(argname_index[1]) + "_threshold",
                    "post_hist",
                )

            elif self._algo in ["avg", "abs_max", "mse", "emd", "ptf"]:
                save_info(
                    op_node,
                    out_var_name,
                    self._quantized_threshold,
                    "out_threshold",
                    "post_" + str(self._algo),
                )
                save_info(
                    op_node,
                    out_var_name,
                    self._quantized_threshold,
                    argname_index[0] + str(argname_index[1]) + "_threshold",
                    "post_" + str(self._algo),
                )
            elif self._algo == "min_max":
                save_info(
                    op_node,
                    out_var_name,
                    self._quantized_var_min,
                    "out_min",
                    "post_min_max",
                )
                save_info(
                    op_node,
                    out_var_name,
                    self._quantized_var_max,
                    "out_max",
                    "post_min_max",
                )

        for block_id in range(len(self._program.blocks)):
            for op in self._program.blocks[block_id].ops:
                if op.type in (
                    self._quantizable_op_type + self._out_scale_op_list
                ):
                    out_var_names = utils._get_op_output_var_names(op)
                    for var_name in out_var_names:
                        analysis_and_save_info(op, var_name)

    def _collect_dynamic_quantize_op_threshold(self, target_ops_type):
        """
        Collect and save the weight threshold for dynamic quantize ops,
        such as lstm and gru.
        Args:
            target_ops_type(list): the op type of target ops
        Returns:
            None
        """

        target_ops = []
        for index in range(self._program.num_blocks):
            for op in self._program.block(index).ops:
                if op.type in target_ops_type:
                    target_ops.append(op)

        quantization_type = str("post_" + self._algo).lower()
        persistable_var_names = _all_persistable_var_names(self._program)
        for op in target_ops:
            for var_name in utils._get_op_input_var_names(op):
                if var_name in persistable_var_names:
                    var_data = utils.load_variable_data(self._scope, var_name)
                    threshold = float(np.max(np.abs(var_data)))
                    argname, index = utils._get_input_name_index(op, var_name)
                    op._set_attr(argname + str(index) + "_threshold", threshold)
                    op._set_attr("quantization_type", quantization_type)
                    op._set_attr("bit_length", self._weight_bits)
                    op._set_attr("with_quant_attr", True)

    def _get_hist_scaling_factor(self, hist, hist_edges):
        '''
        Using the hist method to get the scaling factor.
        '''
        threshold_rate = self._hist_percent
        hist = hist / float(sum(hist))
        hist_sum = 0
        hist_index = 0
        for i in range(len(hist)):
            hist_sum += hist[i]
            if hist_sum >= threshold_rate:
                hist_index = i + 1
                break
        bin_width = hist_edges[1] - hist_edges[0]
        return (hist_index - 0.5) * bin_width


class PostTrainingQuantizationProgram(PostTrainingQuantization):
    def __init__(
        self,
        executor,
        program,
        feed_list=None,
        fetch_list=None,
        scope=None,
        batch_generator=None,
        sample_generator=None,
        data_loader=None,
        batch_size=10,
        batch_nums=None,
        algo="KL",
        hist_percent=0.99999,
        quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
        round_type='round',
        learning_rate=0.001,
        is_full_quantize=False,
        bias_correction=False,
        activation_bits=8,
        weight_bits=8,
        activation_quantize_type='range_abs_max',
        weight_quantize_type='channel_wise_abs_max',
        onnx_format=False,
        freeze_model=True,
        optimize_model=False,
        is_use_cache_file=False,
        skip_tensor_list=None,
        same_scale_tensor_list=None,
        cache_dir=None,
        scale_dict=None,
        return_graph=True,
    ):
        super().__init__(
            executor,
            scope,
            None,
            None,
            None,
            batch_generator,
            sample_generator,
            data_loader,
            batch_size,
            batch_nums,
            algo,
            hist_percent,
            quantizable_op_type,
            round_type,
            learning_rate,
            is_full_quantize,
            bias_correction,
            activation_bits,
            weight_bits,
            activation_quantize_type,
            weight_quantize_type,
            onnx_format,
            freeze_model,
            optimize_model,
            is_use_cache_file,
            skip_tensor_list,
            same_scale_tensor_list,
            cache_dir,
            scale_dict,
            return_graph,
        )
        self.FLAG = False
        self._program = program
        if self._program is not None:
            self.FLAG = True
        assert feed_list is not None, "Feed list should not be None."
        assert fetch_list is not None, "Fetch list should not be None."
        self._feed_list = feed_list
        self._fetch_list = fetch_list


class WeightQuantization:
    _supported_quantizable_op_type = ['conv2d', 'depthwise_conv2d', 'mul']
    _supported_weight_quantize_type = ['channel_wise_abs_max', 'abs_max']

    def __init__(self, model_dir, model_filename=None, params_filename=None):
        '''
        This class quantizes the weight of some ops to reduce the size of model
        or improve the perforemace.

        Args:
            model_dir(str): The path of the fp32 model that will be quantized,
                and the model and params files are under the path.
            model_filename(str, optional): The name of file to load the inference
                program. If it is None, the default filename '__model__' will
                be used. Default is 'None'.
            params_filename(str, optional): The name of file to load all parameters.
                When all parameters were saved in a single binary file, set it
                as the real filename. If parameters were saved in separate files,
                set it as 'None'. Default is 'None'.
        '''
        self._model_dir = model_dir
        self._model_filename = model_filename
        self._params_filename = params_filename

    def quantize_weight_to_int(
        self,
        save_model_dir,
        save_model_filename=None,
        save_params_filename=None,
        quantizable_op_type=["conv2d", "mul"],
        weight_bits=8,
        weight_quantize_type="channel_wise_abs_max",
        generate_test_model=False,
        threshold_rate=0.0,
    ):
        '''
        In order to reduce the size of model, this api quantizes the weight
        of some ops from float32 to int8/16. In the inference stage, the
        quantized weight will be dequantized to float32 again.

        Args:
            save_model_dir(str): The path to save the quantized model.
            save_model_filename(str, optional): The name of file to
                save the inference program. If it is None, the default
                filename '__model__' will be used. Default is 'None'.
            save_params_filename(str, optional): The name of file to
                save all parameters. If it is None, parameters were
                saved in separate files. If it is not None, all
                parameters were saved in a single binary file.
            quantizable_op_type(list[str], optional): The list of ops
                that will be quantized, and the quantized ops should be
                contained in ["conv2d", "depthwise_conv2d", "mul"].
                Default is ["conv2d","mul"].
            weight_bits(int, optional): The bits for the quantized weight,
                and it should be 8 or 16. Default is 8.
            weight_quantize_type(str, optional): quantization type for weights,
                support 'channel_wise_abs_max' and 'abs_max'. Set it as
                'channel_wise_abs_max', the accuracy performs better.
            generate_test_model(bool, optional): If set generate_test_model
                as True, it saves a fake quantized model, in which the weights
                are quantized and dequantized. We can use PaddlePaddle to load
                the fake quantized model and test the accuracy on GPU or CPU.
            threshold_rate(float, optional): This api uses abs_max methd to
                quantize the weight from float32 to int8/16, and the abs max
                value is important for quantization diff. When the abs_max
                value is far away from the center of the numerical distribution,
                we can set threshold_rate between 1e-6 and 1e-8, so the abs max
                value will be optimized. Default is 0.0.
        '''
        for op_type in quantizable_op_type:
            assert op_type in self._supported_quantizable_op_type, (
                "Input error:"
                + op_type
                + " is not supported for weight quantization."
            )
        assert weight_bits in [
            8,
            16,
        ], "Input error: weight_bits should be 8 or 16."
        assert (
            weight_quantize_type in self._supported_weight_quantize_type
        ), "Input error: weight_quantize_type should in {}".format(
            self._supported_weight_quantize_type
        )

        quantized_model_dir = os.path.join(save_model_dir, "quantized_model")
        self._quantize_weight_to_int(
            quantized_model_dir,
            save_model_filename,
            save_params_filename,
            quantizable_op_type,
            weight_bits,
            weight_quantize_type,
            False,
            threshold_rate,
        )

        if generate_test_model:
            test_model_dir = os.path.join(save_model_dir, "test_model")
            self._quantize_weight_to_int(
                test_model_dir,
                save_model_filename,
                save_params_filename,
                quantizable_op_type,
                weight_bits,
                weight_quantize_type,
                True,
                threshold_rate,
            )

    def convert_weight_to_fp16(self, save_model_dir):
        """
        Convert all presistable vars from fp32 to fp16.
        Note that, this api only changes the data type of variables in
        __params__ file, and the __model__ file remains unchanged.

        Args:
            save_model_dir(str): The path to save the fp16 model.
        """

        # Load model
        place = core.CPUPlace()
        exe = Executor(place)
        scope = global_scope()
        [infer_program, feed_list, fetch_list] = io.load_inference_model(
            dirname=self._model_dir,
            executor=exe,
            model_filename=self._model_filename,
            params_filename=self._params_filename,
        )

        # Clone and save fp16 weights
        save_program = framework.Program()
        save_block = save_program.global_block()
        save_var_map = {}

        for var in infer_program.list_vars():
            if (
                (var.type == core.VarDesc.VarType.RAW)
                or (not var.persistable)
                or (var.name in ['feed', 'fetch'])
                or (var.dtype != core.VarDesc.VarType.FP32)
            ):
                continue

            # new_var = _clone_var_to_block_(var, save_block)
            new_var = save_block._clone_variable(var)
            if self._params_filename is not None:
                save_var_map[new_var.name] = new_var
            else:
                save_file_path = os.path.join(
                    os.path.normpath(save_model_dir), new_var.name
                )
                save_block.append_op(
                    type='save',
                    inputs={'X': [new_var]},
                    outputs={},
                    attrs={
                        'file_path': os.path.normpath(save_file_path),
                        'save_as_fp16': True,
                    },
                )

        if self._params_filename is not None:
            save_var_list = []
            for name in sorted(save_var_map.keys()):
                save_var_list.append(save_var_map[name])

            saved_params_var = save_block.create_var(
                type=core.VarDesc.VarType.RAW,
                name=unique_name.generate("saved_params"),
            )
            saved_params_var.desc.set_persistable(True)

            save_path = os.path.join(
                os.path.normpath(save_model_dir), self._params_filename
            )
            save_block.append_op(
                type='save_combine',
                inputs={'X': save_var_list},
                outputs={'Y': saved_params_var},
                attrs={'file_path': save_path, 'save_as_fp16': True},
            )

        save_program._sync_with_cpp()
        exe.run(save_program)

        # Copy model
        model_filename = (
            "__model__"
            if self._model_filename is None
            else self._model_filename
        )
        src_model = os.path.join(self._model_dir, model_filename)
        dest_model = os.path.join(save_model_dir, model_filename)
        shutil.copyfile(src_model, dest_model)

    def _quantize_weight_to_int(
        self,
        save_model_dir,
        save_model_filename,
        save_params_filename,
        quantizable_op_type,
        weight_bits,
        weight_quantize_type,
        for_test,
        threshold_rate,
    ):
        """
        Generate quantized model or fake quantized model.
        """
        # Load model
        place = core.CPUPlace()
        exe = Executor(place)
        scope = global_scope()
        [program, feed_list, fetch_list] = io.load_inference_model(
            dirname=self._model_dir,
            executor=exe,
            model_filename=self._model_filename,
            params_filename=self._params_filename,
        )

        quantized_ops = []
        for index in range(program.num_blocks):
            block = program.block(index)
            for op in block.ops:
                if op.type in quantizable_op_type:
                    quantized_ops.append(op)

        # Quantize weights
        persistable_var_names = _all_persistable_var_names(program)
        for op in quantized_ops:
            for var_name in op.input_arg_names:
                if var_name in persistable_var_names:
                    if weight_quantize_type == "abs_max":
                        self._weight_abs_max_quantization(
                            scope,
                            place,
                            weight_bits,
                            threshold_rate,
                            op,
                            var_name,
                            for_test,
                        )
                    elif weight_quantize_type == "channel_wise_abs_max":
                        self._weight_channel_wise_abs_max_quantization(
                            scope, place, weight_bits, op, var_name, for_test
                        )

        io.save_inference_model(
            dirname=save_model_dir,
            feeded_var_names=feed_list,
            target_vars=fetch_list,
            executor=exe,
            main_program=program,
            model_filename=save_model_filename,
            params_filename=save_params_filename,
        )

    def _weight_abs_max_quantization(
        self, scope, place, weight_bits, threshold_rate, op, var_name, for_test
    ):
        '''
        Use abs_max method to quantize weight.
        '''
        quantize_range = (1 << (weight_bits - 1)) - 1
        save_weight_dtype = np.int8 if weight_bits == 8 else np.int16

        # Get quantized scale and weight data
        weight_data = utils.load_variable_data(scope, var_name)
        if abs(threshold_rate) < 1e-10:
            threshold_value = np.max(np.abs(weight_data))
        else:
            threshold_value = self._calculate_threshold(
                weight_data, threshold_rate
            )
            weight_data[weight_data > threshold_value] = threshold_value
            weight_data[weight_data < -threshold_value] = -threshold_value
        scale = threshold_value / quantize_range
        quantized_weight_data = np.around(weight_data / scale).astype(
            save_weight_dtype
        )

        # Set weight data
        if not for_test:
            utils.set_variable_data(
                scope, place, var_name, quantized_weight_data
            )
        else:
            dequantized_weight_data = (quantized_weight_data * scale).astype(
                np.float32
            )
            utils.set_variable_data(
                scope, place, var_name, dequantized_weight_data
            )

        # Save info
        op._set_attr('quantization_type', 'post_weight_abs_max')
        op._set_attr('quantize_weight_bits', weight_bits)
        op._set_attr(var_name + "_quant_scale", [scale])  # Save as list
        op._set_attr("with_quant_attr", True)

    def _weight_channel_wise_abs_max_quantization(
        self, scope, place, weight_bits, op, var_name, for_test
    ):
        '''
        Use channel_wise_abs_max method to quantize weight.
        '''
        quantize_range = (1 << (weight_bits - 1)) - 1
        save_weight_dtype = np.int8 if weight_bits == 8 else np.int16

        # Get quantized scale and weight data
        weight_data = utils.load_variable_data(scope, var_name)
        if op.type == "mul":
            scales, quantized_weight_data = self._mul_channel_wise_quantization(
                weight_data, quantize_range, save_weight_dtype
            )
        elif op.type in ["conv2d", "depthwise_conv2d"]:
            (
                scales,
                quantized_weight_data,
            ) = self._conv_channel_wise_quantization(
                weight_data, quantize_range, save_weight_dtype
            )
        else:
            _logger.error(op.type + " is not supported by weight quantization")

        # Set weight data
        if not for_test:
            utils.set_variable_data(
                scope, place, var_name, quantized_weight_data
            )
        else:
            if op.type == "mul":
                dequantized_weight_data = self._mul_channel_wise_dequantization(
                    quantized_weight_data, scales
                )
            elif op.type in ["conv2d", "depthwise_conv2d"]:
                dequantized_weight_data = (
                    self._conv_channel_wise_dequantization(
                        quantized_weight_data, scales
                    )
                )
            else:
                _logger.error(
                    op.type + " is not supported by weight quantization"
                )
            utils.set_variable_data(
                scope, place, var_name, dequantized_weight_data
            )

        # Save info
        op._set_attr('quantization_type', 'post_weight_channel_wise_abs_max')
        op._set_attr('quantize_weight_bits', weight_bits)
        op._set_attr(var_name + "_quant_scale", scales)
        op._set_attr("with_quant_attr", True)

    def _conv_channel_wise_quantization(
        self, weight_data, quantize_range, save_weight_dtype
    ):
        '''
        Get channel wise scale for the weights of conv2d and depthwise_conv2d,
        and quantize the weights.
        '''
        scales = []
        quantized_weight_data = np.zeros_like(
            weight_data, dtype=save_weight_dtype
        )
        channel_num = weight_data.shape[0]
        for i in range(channel_num):
            scale = np.max(np.abs(weight_data[i])) / quantize_range
            scales.append(scale)
            quantized_weight_data[i] = np.around(weight_data[i] / scale).astype(
                save_weight_dtype
            )
        return scales, quantized_weight_data

    def _conv_channel_wise_dequantization(self, quantized_weight_data, scales):
        '''
        For conv2d and depthwise_conv2d, dequantize the weights to fp32.
        '''
        dequantized_weight_data = np.zeros_like(
            quantized_weight_data, dtype=np.float32
        )
        for i in range(len(scales)):
            dequantized_weight_data[i] = (
                quantized_weight_data[i] * scales[i]
            ).astype(np.float32)
        return dequantized_weight_data

    def _mul_channel_wise_quantization(
        self, weight_data, quantize_range, save_weight_dtype
    ):
        '''
        Get channel wise scale for the weights of conv2d and depthwise_conv2d,
        and quantize the weights.
        '''
        scales = []
        quantized_weight_data = np.zeros_like(
            weight_data, dtype=save_weight_dtype
        )
        channel_num = weight_data.shape[-1]
        for i in range(channel_num):
            scale = np.max(np.abs(weight_data[:, i])) / quantize_range
            scales.append(scale)
            quantized_weight_data[:, i] = np.around(
                weight_data[:, i] / scale
            ).astype(save_weight_dtype)
        return scales, quantized_weight_data

    def _mul_channel_wise_dequantization(self, quantized_weight_data, scales):
        '''
        For mul, dequantize the weights to fp32.
        '''
        dequantized_weight_data = np.zeros_like(
            quantized_weight_data, dtype=np.float32
        )
        for i in range(len(scales)):
            dequantized_weight_data[:, i] = (
                quantized_weight_data[:, i] * scales[i]
            ).astype(np.float32)
        return dequantized_weight_data

    def _calculate_threshold(self, input, threshold_rate, histogram_bins=5000):
        input_abs = np.abs(input)
        hist, hist_edeges = np.histogram(
            input_abs, bins=histogram_bins, range=(0, np.max(input_abs))
        )
        hist = hist / float(sum(hist))
        hist_sum = 0
        hist_index = 0
        for i in range(len(hist)):
            hist_sum += hist[i]
            if hist_sum >= 1.0 - threshold_rate:
                hist_index = i + 1
                break
        bin_width = hist_edeges[1] - hist_edeges[0]
        return hist_index * bin_width